A VLM backbone with a flow-matching action expert, trained across many robots, doing long dexterous tasks like laundry folding.VLM 主干加流匹配动作专家，跨多种机器人训练，能完成叠衣服等长时程灵巧任务。
@misc{black2024vision,
title = {{$π_0$: A Vision-Language-Action Flow Model for General Robot Control}},
author = {Kevin Black and Noah Brown and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Lucy Xiaoyang Shi and James Tanner and Quan Vuong and Anna Walling and Haohuan Wang and Ury Zhilinsky},
year = {2024},
eprint = {2410.24164},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2410.24164},
}
@misc{lambeta2020digit,
title = {{DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation}},
author = {Mike Lambeta and Po-Wei Chou and Stephen Tian and Brian Yang and Benjamin Maloon and Victoria Rose Most and Dave Stroud and Raymond Santos and Ahmad Byagowi and Gregg Kammerer and Dinesh Jayaraman and Roberto Calandra},
year = {2020},
eprint = {2005.14679},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2005.14679},
}
A foundation world model pretrained on 44k hours of egocentric human video, using continuous latent actions as proxies for missing labels, then post-trained on small robot datasets.在 4.4 万小时第一视角人类视频上预训练的基础世界模型，用连续潜动作代替缺失的动作标签，再用小规模机器人数据后训练。
@misc{gao2026dreamdojo,
title = {{DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos}},
author = {Shenyuan Gao and William Liang and Kaiyuan Zheng and Ayaan Malik and Seonghyeon Ye and Sihyun Yu and Wei-Cheng Tseng and Yuzhu Dong and Kaichun Mo and Chen-Hsuan Lin and Qianli Ma and Seungjun Nah and Loic Magne and Jiannan Xiang and Yuqi Xie and Ruijie Zheng and Dantong Niu and You Liang Tan and K. R. Zentner and George Kurian and Suneel Indupuru and Pooya Jannaty and Jinwei Gu and Jun Zhang and Jitendra Malik and Pieter Abbeel and Ming-Yu Liu and Yuke Zhu and Joel Jang and Linxi "Jim" Fan},
year = {2026},
eprint = {2602.06949},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.06949},
}
World Models世界模型Dexterous hand灵巧手human-videodexterouslatent-action
HIL-SERL — real-world RL with human corrections, reported to reach near-perfect success on precise assembly and dynamic tasks after a few hours of training.HIL-SERL：结合人类纠正的真机强化学习，报告称在精密装配与动态任务上经数小时训练即可接近满分成功率。
@misc{luo2024precise,
title = {{Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}},
author = {Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
year = {2024},
eprint = {2410.21845},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.21845},
}
Commercially licensed 3B-parameter humanoid VLA: a Cosmos-Reason2 backbone plans and a diffusion transformer outputs motor commands. NVIDIA reports training on about 20,854 hours of human egocentric video.可商用的 30 亿参数人形 VLA：Cosmos-Reason2 主干负责推理规划，扩散 Transformer 输出电机指令。NVIDIA 称用约 20,854 小时人类第一视角视频训练。
Learns to predict future hand-object states from human interaction datasets and uses the predicted object states as a general RL reward, reducing reward engineering; deployed on different hands (AAAI oral).从人手交互数据学习预测未来手物状态，并把预测的物体状态作为通用强化学习奖励，减少逐任务奖励设计；已部署到不同灵巧手（AAAI 口头报告）。
Trains a VLA largely on world-model-generated data (re-rendered real videos, new viewpoints, sim2real, human-to-robot video); reports gains over π0 on dexterous and mobile tasks, plus a 402M variant for Jetson Orin.VLA 训练大量依赖世界模型生成的数据（真实视频重渲染、新视角、仿真转真实、人类视频转机器人）；论文报告在灵巧与移动操作任务上优于 π0，并提供可在 Jetson Orin 上运行的 4.02 亿参数小模型。
@misc{team2025gigabrain,
title = {{GigaBrain-0: A World Model-Powered Vision-Language-Action Model}},
author = {GigaBrain Team and Angen Ye and Boyuan Wang and Chaojun Ni and Guan Huang and Guosheng Zhao and Haoyun Li and Jie Li and Jiagang Zhu and Lv Feng and Peng Li and Qiuping Deng and Runqi Ouyang and Wenkang Qin and Xinze Chen and Xiaofeng Wang and Yang Wang and Yifan Li and Yilong Li and Yiran Ding and Yuan Xu and Yun Ye and Yukun Zhou and Zhehao Dong and Zhenan Wang and Zhichao Liu and Zheng Zhu},
year = {2025},
eprint = {2510.19430},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2510.19430},
}
From one human demo: retarget hand-object motion to a robot reference, train residual RL in sim to track object keypoints, transfer zero-shot with careful system ID. Scissors and screwdriver use on two hands.从一条人类演示出发：把手-物运动重定向为机器人参考，在仿真中用残差强化学习跟踪物体关键点，并通过细致的系统辨识零样本迁移。在两种多指手上完成用剪刀、拧螺丝刀等任务。
@misc{feng2026minimalist,
title = {{A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation}},
author = {Yunhai Feng and Natalie Leung and Jiaxuan Wang and Lujie Yang and Haozhi Qi and Preston Culbertson},
year = {2026},
eprint = {2607.11874},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.11874},
}
Pretrains a dexterous RL policy on generic object reposing, then post-trains downstream tasks with BC distillation, critic warm-up and conservative updates. Zero-shot sim-to-real on 23-DoF and 29-DoF hand-arm systems.先在通用物体重定位任务上用强化学习预训练灵巧策略，再用行为克隆蒸馏、critic 预热和保守更新后训练下游任务。在 23 与 29 自由度手-臂系统上零样本仿真到真机。
@misc{lee2026adept,
title = {{ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning}},
author = {Jayjun Lee and Jessica Yin and Asif Rana and Nicholas Blauch and Octi Zhang and Sam Mady and Mohak Bhardwaj and Nima Fazeli and Nathan Ratliff and Karl Van Wyk and Ankur Handa},
year = {2026},
eprint = {2608.19182},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2608.19182},
}
Active in-hand 3D reconstruction: estimates per-ray reconstruction uncertainty to pick the next view, then an in-hand rotation policy turns the object to expose it. Better quality and efficiency than non-active rotation.主动式手内三维重建：沿候选视线估计各方向的重建不确定性来选择下一最佳视角，再由手内旋转策略把物体转到该视角。重建质量与效率均优于非主动旋转。
Language-to-dexterous-manipulation as modulated tracking: a distilled generalist tracker plus an RL-trained modulator that keeps adjusting the reference, object latent and position target; zero-shot sim-to-real.把语言条件灵巧操作变成“可调制的跟踪”：蒸馏得到的通用跟踪器加 RL 训练的调制器，在执行中持续修正参考轨迹、物体隐表征与位置目标，抑制误差累积；零样本迁移到真机。
@misc{adalibieke2026adadextrack,
title = {{AdaDexTrack: Dynamic Modulation for Adaptive and Generalizable Dexterous Manipulation Tracking}},
author = {Jianibieke Adalibieke and Qianwei Han and Xueyi Liu and Yuzhe Qin and Li Yi},
year = {2026},
eprint = {2610.33852},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33852},
}
AdvDex trains a dexterous VLA on human and robot demos: OmniShare human data with touch, a joint-aligned action space (wrist pose + 15 finger joints) shared by hands and grippers, and adversarial removal of body cues.AdvDex 用人类与机器人示教训练灵巧操作 VLA：含触觉的人类示教数据集 OmniShare、人手/灵巧手/夹爪共用的关节对齐动作空间（腕部位姿 + 15 个手指关节），并以对抗学习去除本体相关特征。
@misc{zhao2026advdex,
title = {{AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning}},
author = {Zhiyue Zhao and Jingyi Wu and Hairuo Liu and Mingyu Liu and Liyang Li and Hengdi Zhang and Tong He and Zhengxue Cheng},
year = {2026},
eprint = {2608.14028},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2608.14028},
}
Low-calibration hand retargeting for teleop: self-supervised fingertip correspondence plus a few human-guided anchors, and a contact classifier to refine pinch poses; tested on several dexterous hands.面向遥操作的灵巧手动作重定向，几乎无需标定：自监督学习指尖对应关系，再用少量人工引导的锚点约束映射，并以接触分类器细化捏取姿态；在多种灵巧手上验证。
@misc{wang2026anydexrt,
title = {{AnyDexRT: Calibration-Efficient Dexterous Hand Retargeting with Few-Shot Human Guidance}},
author = {Chenxi Wang and Ying Feng and Hongjie Fang and Shangning Xia and Lixin Yang and Chuan Wen and Cewu Lu},
year = {2026},
eprint = {2607.08341},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.08341},
}
Category-level in-hand manipulation of articulated objects: procedural objects and functional grasps, then two-stage training with articulation physics randomization, reward curriculum and latent distillation.类别级的铰接物体手内操作：程序化生成物体与功能性抓取，再用两阶段训练（铰接物理随机化、奖励课程、潜表示蒸馏）学习策略，可泛化到未见实例。
@misc{yang2026artmanip,
title = {{ArtManip: Category-Level Articulated In-Hand Manipulation}},
author = {Yang Yang and Tengyu Liu and Puhao Li and Zeyuan Chen and Yuyang Li and Xingwan Wang and Yingying Wu and Zhaopeng Cui and Siyuan Huang},
year = {2026},
eprint = {2609.12498},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.12498},
}
RL for mating two parts within one dexterous hand (capping a marker, a syringe, a bottle) with no second arm. Trained in sim, transfers zero-shot with a single camera.用强化学习在单只灵巧手中完成两个零件的装配（如给马克笔盖帽、注射器、瓶子），不用第二只手臂；仿真训练，单相机零样本迁移到真机。
@misc{pei2026assembling,
title = {{Assembling Two Parts in One Hand}},
author = {Liuao Pei and Tianyue Wu and Hui Zhang and Ping Luo and Jie Song},
year = {2026},
eprint = {2609.10137},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.10137},
}
AutoDex runs real dexterous-grasp trials, labels outcomes and resets objects with little human help: 3,500+ trials on 100 objects (Allegro, Inspire); about 0.2 human-hours per 500 trajectories vs 9.3 by teleop.AutoDex 自动执行真机灵巧抓取、标注结果并复位物体，几乎无需人工：在 100 个物体上用 Allegro 与 Inspire 手采集 3500 多次试验；每 500 条轨迹约需 0.2 人时，遥操作需 9.3 人时。
@misc{choi2026autodex,
title = {{AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection}},
author = {Mingi Choi and Gunhee Kim and Jisoo Kim and Taeksoo Kim and Taeyun Ha and Jongbin Lim and Hanbyul Joo},
year = {2026},
eprint = {2606.23689},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.23689},
}
A cheap drop-in finger module for parallel grippers: two soft belts keep normal open/close and add in-hand translation, pitch and roll; used with MPC, a light 10-DoF teleop interface and learned policies.低成本的平行夹爪升级手指模组：双软皮带结构保留常规开合，并增加手内平移、俯仰与翻滚三个自由度；配合 MPC、轻量的 10 自由度遥操作接口和学习策略使用。
@misc{zhang2026belt,
title = {{Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation}},
author = {Boya Zhang and Andreas Zell and Georg Martius},
year = {2026},
eprint = {2606.20193},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.20193},
}
106 sim tasks for two dexterous hands along five axes (generalization, instructions, few demos, long horizon, memory), each with an out-of-domain twin; 8,080 teleoperated demos; seven VLAs benchmarked.双灵巧手仿真基准：106 个任务按泛化、指令遵循、少示教、长时程、记忆五个维度组织，每个任务配有域外变体；提供 8,080 条遥操作示教，并在统一协议下评测了七个 VLA 模型。
@misc{wang2026bidex,
title = {{BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks}},
author = {Yong Wang and Kecheng Zheng and Xing Zhu and Chao Xu and Yujun Shen and Shengze Cai},
year = {2026},
eprint = {2610.25459},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25459},
}
CoRL 2026 Spotlight. A large-scale bimanual dexterous grasp dataset, built with a two-stage synthesis pipeline, together with a learning-based grasp generation framework.CoRL 2026 Spotlight。大规模双手灵巧抓取数据集（由两阶段合成流程生成），并配套基于学习的抓取生成框架。
@misc{lin2026bidexgrasp,
title = {{BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes}},
author = {Mu Lin and Yi-Lin Wei and Jiaxuan Chen and Yuhao Lin and Shuoyu Chen and Jiangran Lyu and Jiayi Chen and Yansong Tang and He Wang and Wei-Shi Zheng},
year = {2026},
eprint = {2604.06589},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.06589},
}
Tactile-only blind grasping on a LEAP Hand: calibrate a digital twin to reproduce real touch signals, pretrain a layout-aware tactile encoder, and distill object-specific RL experts into a diffusion policy.在 LEAP 手上实现只凭触觉的盲抓：先构建能复现真实触觉信号的校准数字孪生，再自监督预训练感知传感器布局的触觉编码器，最后把各物体的强化学习专家蒸馏为扩散策略。
@misc{luo2026blind,
title = {{Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning}},
author = {Shengcheng Luo and Xiyan Huang and Zhe Xu and Wanlin Li and Ziyuan Jiao and Chenxi Xiao},
year = {2026},
eprint = {2606.11767},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.11767},
}
Retargets human hand demos (video, mocap, tactile gloves) by keeping contact-force targets, not just kinematics; a tactile tracking controller replays them. Reports contact kept on 43–78% of frames vs <10%.把人手示教（视频、动捕、触觉手套）重定向到灵巧手时显式保留接触力目标而不只是运动学，并用触觉跟踪控制器回放；报告 43–78% 的接触帧保持接触，纯运动学方法不到 10%。
@misc{dai2026card,
title = {{CARD: Contact-Anchored Retargeting for Dexterous Manipulation}},
author = {Cunxi Dai and Botao He and Francois R Hogan and Wei Pan and Rao Fu and Wen-Han Hsieh and Ruoshi Liu and Jitendra Malik and Guanya Shi and Akash Sharma},
year = {2026},
eprint = {2610.36539},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36539},
}
Mobile dexterous grasping learned in sim: a whole-body controller does reaching and active vision, local RL experts the object, all distilled into one point-cloud policy; reports zero-shot real transfer.在仿真中学习移动灵巧抓取：全身控制器负责底盘-手臂协同到达与主动视觉，局部强化学习专家负责物体交互，二者蒸馏成一个基于点云的视觉策略；报告可零样本迁移到真实世界的数百个物体与场景。
@misc{yang2026cowboy,
title = {{COWBOY: A Scalable Sim-to-Real Framework for Learning Contextual Whole-Body Manipulation}},
author = {Jiahui Yang and Jason Jingzhou Liu and Ray Muxin Liu and Andrew E. Wang and Kenneth Shaw and Deepak Pathak and Ruslan Salakhutdinov},
year = {2026},
eprint = {2610.28948},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28948},
}
Helps RL explore contact-rich tasks by resetting episodes with a contact-aware constrained state sampler, covering diverse contact modes; trains universal in-hand and whole-body policies.用感知接触的约束状态采样器重置回合，覆盖多样接触模式，帮助 RL 探索接触丰富任务；可训练通用的手内和全身操作策略。
@misc{toussaint2026combining,
title = {{Combining Constrained Sampling and Reinforcement Learning for Manipulation}},
author = {Marc Toussaint and Cornelius V. Braun and Armand Jordana and Sayantan Auddy and Eckart Cobo-Briesewitz and Denis Shcherba and Tilman Burghoff and Justin Carpentier},
year = {2026},
eprint = {2602.08557},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.08557},
}
Robot-free dexterous demos with shared contact geometry, fingertip RGB and contact-first retargeting. Reports at least 50 points higher final-stage success than baselines on LEAP and Wuji hands; hardware is open.免机器人灵巧演示框架：共享接触几何、指尖 RGB 视角、接触对齐优先的重定向，加上预测接触面轨迹的层次策略。报告在 LEAP 和 Wuji 手的六组手-任务上，最终阶段成功率比基线至少高 50 个百分点；硬件与软件公开。
@misc{xu2026contact,
title = {{Contact Imitation Interface: Transferring Dexterous Skills by Showing How Contact Unfolds}},
author = {Zhixuan Xu and Yichen Li and Bingchen Wu and Rui Wang and Yiwen Hou and Zixuan Liu and Tianyu Qiu and Lin Shao},
year = {2026},
eprint = {2610.41028},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41028},
}
Uses motor current as a touch signal on low-cost dexterous hands: a model predicts a compliant PD target position whose error yields the right grasp force, with no tactile or force sensors.把电机电流当作触觉：在低成本灵巧手上预测一个“柔顺参考位置”，让 PD 控制器的位置误差产生合适的抓握力，无需触觉或力传感器。
@misc{ma2026current,
title = {{Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation}},
author = {Chenyang Ma and Yunchao Yao and Zhenyu Wei and Ruogu Li and Daniel Szafir and Mingyu Ding},
year = {2026},
eprint = {2607.03529},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.03529},
}
One two-hand policy (PPO + BC on its own verified successes) tracks retargeted human hand-object motions; ships BiHOI (1,575 sequences, 229 objects). Reports 86.8% on unseen trajectories, 57.3% on new objects.用一个双手跟踪策略（PPO 结合对自身成功执行的行为克隆）复现重定向后的人手-物体交互动作，并发布 BiHOI 数据集（1575 段双手序列、229 个物体）。论文报告在未见轨迹上成功率 86.8%、新物体上 57.3%。
@misc{sun2026donic,
title = {{DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations}},
author = {Mingze Sun and Ziang Cao and Yuan Fei and Qiankuan Tao and Zihan Wang and Tianqi Liu and Junhao Chen and Haiwen Diao and Fangzhou Hong and Zhaoxi Chen and Ziwei Liu},
year = {2026},
eprint = {2610.29997},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29997},
}
Dexterous visuotactile VLA on a mixture-of-transformers: contact-aware gating decides when touch matters; the model jointly imagines future visual and tactile latents. Reports 71% average success on contact-rich tasks.基于混合 Transformer 的灵巧视触觉 VLA：接触感知门控决定何时引入触觉，并联合想象未来的视觉与触觉潜变量。报告在富接触任务上平均成功率 71%。
@misc{fu2026decal,
title = {{DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination}},
author = {Yankai Fu and Ning Chen and Junkai Zhao and Heng Zhang and Guocai Yao and Pengwei Wang and Zhongyuan Wang and Shanghang Zhang},
year = {2026},
eprint = {2609.09119},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.09119},
}
Dexterous-manipulation world model that first predicts object motion and deformation in metric 3D from a hand trajectory, then renders video. Reports 27–40% lower moved-point error than PointWorld.灵巧操作世界模型：先根据手部轨迹在度量 3D 空间中显式预测物体运动与形变，再据此渲染视频。报告在 HOT3D、OakInk2、PhysTwin 上运动点误差比 PointWorld 低 27–40%，两步因果生成大幅提速。
@misc{lee2026dexplicit,
title = {{DeXplicit: Making Physical State Explicit in Dexterous Manipulation World Models}},
author = {Adam Lee and Mutian Xu and Yukang Cao and Shulin Tian and Ziwei Liu},
year = {2026},
eprint = {2610.30781},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.30781},
}
Task-agnostic 'any pose to any pose' dexterous policy trained in sim on thousands of objects and conditioned on 3D point tracks; prompted with tracks from generated videos, it runs real tasks zero-shot.在仿真中用数千种物体训练与任务无关的“任意位姿到任意位姿”灵巧策略，以 3D 点轨迹为条件；用生成视频提取的点轨迹作提示，即可零样本完成真实任务。
10 long-horizon and 30 stage-level bimanual dexterous tasks with physically grounded success conditions per stage, so partial progress is measurable; includes verified teleoperated demos.10 个长时序和 30 个阶段级双手灵巧任务，每个阶段都有物理判定的成功条件，可衡量中间进度；提供经过验证的遥操作示范和统一接口。
@misc{ping2026dexarena,
title = {{DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation}},
author = {Haonan Ping and Jianshu Hu and Yimin Wang and Yi Zhu and Pai Liang and Qiyan Jin and Qirui Bao and Ying Qian and Xinyang Ding and Hongfei Qiao and Tianle Xue and Yucheng Bai and Soo Wei Jie and Shuguang Li and Yutong Ban},
year = {2026},
eprint = {2611.02629},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02629},
}
DexFLEX treats fingertip motion commands from teleop or a policy as intent: it proposes short motion chunks, predicts their contact outcome and picks the best; real teleop success rises from 29.2% to 78.3%.DexFLEX 把来自遥操作或策略的指尖运动指令视为意图：生成若干短时动作块、预测其接触结果并择优执行；真实遥操作成功率从 29.2% 提升到 78.3%。
@misc{wan2026dexflex,
title = {{DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity}},
author = {Weikang Wan and Zeyu Lai and Jianyu Hou and Yuchen Zhou and Jiawei Fu and Henrik I Christensen and Hao Su},
year = {2026},
eprint = {2610.14372},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.14372},
}
Trains a world model of 3D hand-point and scene motion, then aligns policy latents to it; at test time only the RGB policy runs, with no depth or future frames. Reports 49.4% → 60.5% on 11 DexJoCo tasks.先训练预测 3D 手部点与场景变化的世界模型，再把策略潜表示对齐到它；部署时只保留 RGB 策略，无需深度或未来预测。在 11 个 DexJoCo 任务上成功率从 49.4% 提升到 60.5%。
@misc{chen2026dexforesight,
title = {{DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight}},
author = {Zhongxi Chen and Yi Yang and Xinyi Chen and Mingzhe Ye and Yaping Li and Zhihong Liu and Congsheng Xu and Haonan Wang and Zhijie Deng},
year = {2026},
eprint = {2611.11166},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11166},
}
Sets dexterous goals as joint hand-object configurations and learns one transfer primitive from synthetic pairs, with no human motion; reports 69.4% on unseen objects in simulation and 61.5% over 200 real transfers.把灵巧操作目标定义为手与物体的联合构型，从合成构型对学习统一的转移基元，无需人类动作；报告仿真未见物体成功率 69.4%，200 次真机转移成功率 61.5%。
@misc{gui2026dexhot,
title = {{DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation}},
author = {Zhewei Gui and Anthony Bolton and Jiyao Zhang and Zeyuan Chen and Qiyang Yan and Tianyu Wang and Hao Dong},
year = {2026},
eprint = {2611.05119},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05119},
}
Dexterous food cutting from a handful of real demos: reconstruct objects, randomize physical parameters within category limits and generate augmented demos in sim, then transfer back to the real hand.只用少量真实示教学习灵巧切割食物：重建物体实例，在类别约束下随机化物理参数，在仿真中生成扩增的灵巧示教，再迁移回真实灵巧手。
@misc{lou2026dexninja,
title = {{DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine}},
author = {Haozhe Lou and Runyi Yang and Wanqi Zhong and Caiyun Liu and Yicheng Liu and Wenhao Liu and Wenlong Ma and Jiawei Xia and Kanzhi Wu and Danda Pani Paudel and Luc Van Gool and Hang Zhao and Yiming Li},
year = {2026},
eprint = {2610.17208},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.17208},
}
A portable hand-held rig for collecting demonstrations with a high-DoF robot hand, using the same hand hardware for collection and deployment, so there is no retargeting from human hands and no full robot setup needed.便携手持式数据采集装置，面向高自由度机器人灵巧手；采集与部署使用同一只机械手，无需从人手动作重定向，也无需搭建完整机器人系统。
@misc{xie2026dexport,
title = {{DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection}},
author = {Amber Xie and Jiawei Fu and Xiaofeng Guo and Carmelo Sferrazza and Guanya Shi and Haozhi Qi},
year = {2026},
eprint = {2610.32969},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32969},
}
@misc{houssaini2026dexsuite,
title = {{DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks}},
author = {Anas Houssaini and Hugo He and Junming Shi and Amin Abyaneh and Ricardo Chahine and Shuo Wen and Mohamad H. Danesh and Charles-Étienne Fréchette and Mariana Sosa Guzman and Mathias Desrochers and Rayan Houssaini and Adam Landa and Theophile Soulie and Charlotte Morissette and Annie Tims and Martino Russi and Jonathan Lussier and Gregory Dudek and Doina Precup and Julen Urain and Hsiu-Chin Lin and David Meger},
year = {2026},
eprint = {2610.37799},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37799},
}
Sim-only RL recipe for in-hand object pose reaching on a LEAP hand, zero-shot to hardware; reports 95.2% of 4,800 unseen goals reached and a tunable residual safety filter that cuts drops by 75%.在仿真中无需人类数据训练 LEAP 灵巧手的手内物体位姿到达策略，零样本迁移到真机；报告在 4800 个未见目标上 95.2% 到达，可调节的残差安全滤波器使掉落减少 75%。
@misc{chuang2026dexterous,
title = {{Dexterous In-Hand Object Pose Reaching with Learned Safety Filters}},
author = {Ian Chuang and Hiya Shah and Vamsi Chilamkur and Iman Soltani and Claire Tomlin},
year = {2026},
eprint = {2610.37652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37652},
}
Diffusion grasp generator conditioned on a graph of the hand's kinematics, so grasps transfer zero-shot to unseen hand designs; comes with a cross-morphology grasp benchmark.以手部运动学结构图为条件的扩散抓取生成模型，可零样本迁移到未见过的机器人手型；同时提出跨手型抓取评测基准。
@misc{wu2026diffmorphgrasp,
title = {{DiffMorphGrasp: A Diffusion Framework Conditioned on Hand Morphology for Cross-Embodiment Grasp Generation}},
author = {Zhiyuan Wu and Xiangyu Zhang and Zhuo Chen and Jiankang Deng and Rolandos Alexandros Potamias and Shan Luo},
year = {2026},
eprint = {2602.00915},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.00915},
}
Reconstructs hand-object interaction from ordinary monocular human videos and retargets it into executable multi-fingered robot actions; beats prior work on hand-object estimation and gives a data-collection playbook.从普通单目人类视频重建手-物交互，并重定向为多指机器人手可执行的动作序列；手-物估计优于已有方法，并给出采集人类数据的实践建议。
@misc{paliwal2026do,
title = {{Do as I Do: Dexterous Manipulation Data from Everyday Human Videos}},
author = {Bhawna Paliwal and Haritheja Etukuru and William Liang and Pieter Abbeel and Nur Muhammad Mahi Shafiullah and Jitendra Malik},
year = {2026},
eprint = {2606.19333},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.19333},
}
Harness that lets coding agents improve real robot policies on their own: automatic reset and verification, parallel rollouts and agent-driven code changes. Reports 99% success on tasks like fastening a zip tie.让编程智能体自主改进真实机器人策略的框架：自动复位与结果验证、多机并行评测，并由智能体分析日志、修改训练代码。报告在扎扎带等灵巧任务上成功率 99%。
@misc{xiao2026enpire,
title = {{ENPIRE: Agentic Robot Policy Self-Improvement in the Real World}},
author = {Wenli Xiao and Jia Xie and Tonghe Zhang and Haotian Lin and Letian "Max" Fu and Haoru Xue and Jalen Lu and Yi Yang and Cunxi Dai and Zi Wang and Jimmy Wu and Guanzhi Wang and S. Shankar Sastry and Ken Goldberg and Linxi "Jim" Fan and Yuke Zhu and Guanya Shi},
year = {2026},
eprint = {2606.19980},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2606.19980},
}
Converts egocentric human videos into robot data: a video with the human replaced by a robot plus a feasible robot action trajectory. Claims the first zero-shot dexterous visuomotor policy with no real-robot demos.把第一人称人类视频转为机器人数据：生成把人替换成机器人的视频，以及满足可行性约束的机器人动作轨迹。声称首次在无真机演示下零样本训练出灵巧视觉运动策略。
@misc{liu2026egoengine,
title = {{EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations}},
author = {Yangcen Liu and Shuo Cheng and Xinchen Yin and Woo Chul Shin and Alfred Cueva and Yiran Yang and Zhenyang Chen and Chuye Zhang and Danfei Xu},
year = {2026},
eprint = {2606.12604},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.12604},
}
Co-trains a VLA on robot demos and noisy egocentric-video actions by weighting each source by reliability and distilling from clean future frames; reports steeper scaling than the baseline up to 30k hours of data.将机器人示教与动作标签噪声较大的第一视角视频联合训练 VLA：按数据源可靠性分配监督权重，并从干净的未来画面蒸馏表征；报告在最多 3 万小时预训练数据范围内扩展斜率高于基线。
@misc{zhang2026egoforesight,
title = {{EgoForesight: Effectively Scaling Policy Learning with Noisy Egocentric Actions}},
author = {Chubin Zhang and Changyuan Wang and Yirui Wang and Ao Li and Shilin Ma and Yue Su and Cai Zhou and Xiuwei Xu and Tianle Zhang and Yansong Tang},
year = {2026},
eprint = {2610.31845},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31845},
}
CoRL 2026 Oral. Trains a VLA on over 20,854 hours of action-labeled egocentric human video to test whether large-scale human data can support fine, high-DoF dexterous manipulation.CoRL 2026 Oral。用超过 20,854 小时带动作标注的第一视角人类视频训练 VLA，检验大规模人类数据能否支撑高自由度精细灵巧操作。
@misc{zheng2026egoscale,
title = {{EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data}},
author = {Ruijie Zheng and Dantong Niu and Yuqi Xie and Jing Wang and Mengda Xu and Yunfan Jiang and Fernando Castañeda and Fengyuan Hu and You Liang Tan and Letian Fu and Trevor Darrell and Furong Huang and Yuke Zhu and Danfei Xu and Linxi Fan},
year = {2026},
eprint = {2602.16710},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.16710},
}
@misc{xu2026fingereye,
title = {{FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing}},
author = {Zhixuan Xu and Yichen Li and Xuanye Wu and Tianyu Qiu and Lin Shao},
year = {2026},
eprint = {2604.20689},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.20689},
}
Puts miniature cameras in each fingertip of a multi-fingered hand and trains a whole-body diffusion policy on third-view plus fingertip views. Reports 56.6% relative gain over external and wrist cameras.在多指手的每个指尖装入微型摄像头，用第三视角加多指尖视角训练全身扩散策略。报告比外部+腕部相机方案平均成功率相对提升 56.6%。
@misc{zhang2026fingervip,
title = {{FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception}},
author = {Zhen Zhang and Weinan Wang and Hejia Sun and Qingpeng Ding and Xiangyu Chu and Guoxin Fang and K. W. Samuel Au},
year = {2026},
eprint = {2604.21331},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.21331},
}
Pretrains a low-level dexterous controller on 355K trajectories built from grasp datasets, then fine-tunes for articulated tool use. Introduces the DexCraft benchmark; reports +33.3 points over DP3 on a real robot.用抓取数据集构造的 35.5 万条轨迹预训练灵巧手底层控制器，再微调到需要操作工具活动部件的任务；提出 DexCraft 基准。报告真机完整任务成功率比 DP3 高 33.3 个百分点。
@misc{yuan2026from,
title = {{From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation}},
author = {Ying Yuan and Xinyu Liu and Sriram Krishna and David Held},
year = {2026},
eprint = {2606.30749},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.30749},
}
Retargets human demos under contact without a differentiable simulator: gradients come from archived sampling rollouts. Reports 92.8% success on 1,352 bimanual OakInk2 demos with no task-specific contact rewards.无需可微仿真器即可在接触动力学下重定向人类示教：从存档的采样轨迹中恢复梯度。报告在 1352 条双手 OakInk2 示教上成功率 92.8%，且不用任务专属接触奖励。
@misc{liu2026grasp,
title = {{GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting}},
author = {Yang Liu and Tong Wang and Yiming Bao and Heng Yang},
year = {2026},
eprint = {2611.05754},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05754},
}
End-to-end in-hand reorientation from RGB and proprioception, with no object pose estimation: distilled from privileged-state teachers, and the target is given as an image. One policy transfers to five real objects.仅凭 RGB 与本体感觉、无需物体位姿估计的端到端手内重定向：由特权状态教师蒸馏而来，目标姿态直接用图像给出。单一策略直接迁移到五个真实物体。
@misc{bhardwaj2026goaldex,
title = {{GoalDex: Learning Image-Goal-Conditioned Policies for Dexterous In-Hand Manipulation}},
author = {Arjun Bhardwaj and Mayank Mittal and Maximum Wilder-Smith and Vaishakh Patil and Marco Hutter},
year = {2026},
eprint = {2611.02171},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02171},
}
Models a dexterous skill as a latent linear (Koopman) system in which visual flow and robot motion co-evolve; it replans when observed and predicted flow diverge. Matches strong baselines with much faster inference.把灵巧技能建模为潜在线性（Koopman）系统，视觉特征流与机器人动作流共同演化；当观测与预测的视觉流偏离时自动重规划。性能与强基线相当，推理快得多。
@misc{han2026going,
title = {{Going with the Flow: Koopman Behavioral Models as Pseudo Planners for Visuo-Motor Dexterity}},
author = {Yunhai Han and Jiaqi Fu and Linhao Bai and Ziyu Xiao and Zhaodong Yang and Yogita Choudhary and Krishna Jha and Chuizheng Kong and Shreyas Kousik and Harish Ravichandar},
year = {2026},
eprint = {2602.07413},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.07413},
}
Learns an object-conditioned human grasp prior from 1.8K human grasps to pick contact modes and wrist starts for force-closure optimisation; synthesises 3.2M grasps from pinches to bimanual, 2–30 cm objects.从 1800 次人类抓取中学习物体条件的人类先验，用来选择接触模式和手腕初值以指导力封闭优化；合成 320 万个抓取，覆盖两指捏取到双手抓取、2–30 cm 物体。
@misc{yu2026hugs,
title = {{HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors}},
author = {Mingrui Yu and Yongpeng Jiang and Yongyi Jia and Kangchen Lv and Li Huang and Yi Ren and Xiang Li},
year = {2026},
eprint = {2607.04554},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.04554},
}
Isaac Sim benchmark with 5 arms, 6 dexterous hands, ~200 assets and 18k human demos over 66 tasks; reports the best trained policy (a WAM) at only 29.1% on Core-50, near 0% on precision-fit and deformable tasks.基于 Isaac Sim 的灵巧操作基准：5 种机械臂、6 种灵巧手、约 200 个资产、1.8 万条人类演示、66 个任务；报告最好的训练策略（一种世界动作模型）在 Core-50 上仅 29.1%，精密装配和柔性物体任务接近 0%。
@misc{luo2026handson,
title = {{HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation}},
author = {Tianwei Luo and Yiming He and Hongjin Wu and Ting Pan and Debin Ren and Haorui Guo and Junlin Yang and Yuxin Zuo and Zhehao Kou and Kaisen Yang and Zhuoqun Lai and Yi Ding and Jiale Yu and Yuchen Zhang and Zijie Qiu and Haozhan Li and Ning Ding},
year = {2026},
eprint = {2610.26765},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26765},
}
Hierarchical real-world RL for functional dexterous tasks: a hand skill handles contact, an arm-hand policy coordinates and triggers it. Reports 98.3% average success on six tasks within one hour of online training.面向功能性灵巧操作的分层真机强化学习：手部技能负责精确接触，臂-手协调策略负责手臂运动与技能触发。报告在六个任务上一小时在线训练即达 98.3% 平均成功率。
@misc{huang2026hifun,
title = {{HiFun: a Hierarchical Framework for Efficient Functional Dexterous Manipulation Learning}},
author = {Linyi Huang and Guowei Huai and Weibin Liu and Shulong Jiang and Ping Tan and Weixuan Zhang and Hui Zhang and Jie Song},
year = {2026},
eprint = {2610.19518},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.19518},
}
Human-in-the-loop RL whose latent world model also predicts when the operator would take over, steering the policy away from risky states. On a 16-DoF LEAP Hand: 96.7% grasp success, up to 84% less intervention.人在回路强化学习：潜在世界模型额外预测操作者何时会接管，并据此让策略远离高风险状态。在 16 自由度 LEAP 手上抓取成功率 96.7%，人工干预最多减少 84%。
@misc{yin2026how,
title = {{How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation}},
author = {Jiaju Yin and Zhenhui Zhang and Lixin Xu and Heng Zhang and Jun Shao and Yating Feng and Arash Ajoudani and Renjing Xu},
year = {2026},
eprint = {2609.06009},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.06009},
}
Uses image editing of simulator renders as a middle step: edit in task semantics, recover 3D state from simulator priors, refine in physics, and turn it into robot supervision, e.g. dexterous grasps and goal states.以对仿真渲染图的图像编辑为中间表示：先编辑注入任务语义，再借助仿真先验恢复 3D 状态并在物理中细化，最后转为机器人可执行的监督，如灵巧抓取与目标状态。
@misc{wang2026im,
title = {{IM-ENGINE: Image Editing for Embodied Data Generation}},
author = {Yian Wang and Junyi Cao and Xiaowen Qiu and Chuang Gan},
year = {2026},
eprint = {2609.06279},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.06279},
}
Generates dexterous grasps as 3D hand keypoints with a plain Transformer flow model, with no contact losses or refinement; reports 76.3% success on Dexonomy and 0.032 s per grasp.以 3D 手部关键点表示灵巧抓取，用普通 Transformer 流模型生成，无需接触损失或后处理优化；报告 Dexonomy 成功率 76.3%，每次抓取 0.032 秒。
@misc{huang2026kpgrasp,
title = {{KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation}},
author = {Yuansen Huang and Jiayi Chen and Haoran Liu and Yubin Ke and Bing Han and Jiangran Lyu and Mi Yan and Li Yi and He Wang},
year = {2026},
eprint = {2606.09314},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.09314},
}
Learns a short-horizon 'what should happen next' intent model from internet human videos and pairs it with embodiment-specific policies trained in parallel sim; the same intent model drives a hand and a gripper.从互联网人类视频学习预测“场景接下来该发生什么”的短时意图模型，再与在大规模并行仿真中训练的本体专用策略结合；同一意图模型可驱动灵巧手和平行夹爪。
Distills sim RL rollouts into a frozen flow-matching action decoder, then learns only a visual interface to its latent from a few demos. Reports beating fine-tuned π0.5 and GR00T N1.7 with 5-10x fewer demos.把仿真中强化学习的经验蒸馏进冻结的流匹配动作解码器，之后只需用少量示教学习从图像到其潜变量的视觉接口。报告用少 5–10 倍的示教超过微调后的 π0.5 与 GR00T N1.7。
@misc{wang2026latent,
title = {{Latent RL Priors for Data-Efficient Dexterous Manipulation}},
author = {Yixiao Wang and Peiyi Chen and Mustafa Mukadam and Shreekant Gayaka and Masayoshi Tomizuka and Son Tran},
year = {2026},
eprint = {2611.08458},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08458},
}
CHORD guides dexterous RL by comparing human and robot motions in object-centric contact wrench space. Also a 4,739-task bimanual benchmark built from MoCap and videos; 82.12% average on 1,831 tasks.CHORD 在以物体为中心的接触力旋量空间中比较人与机器人的动作来引导灵巧操作强化学习，并构建来自动捕与视频的 4739 个双手任务基准；在其中 1831 个任务上平均成功率 82.12%。
@misc{zhu2026learning,
title = {{Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration}},
author = {Xinghao Zhu and Zixi Liu and Shalin Jain and Chenran Li and Milad Noori and Michael Andres Lin and Huihua Zhao and John Welsh and Mrinal Verghese and Wei Liu and Tingwu Wang and Xingye Da and Zhengyi Luo and Vishal Kulkarni and Naema Bhatti and Yuke Zhu and Linxi Fan and Bowen Wen and Danfei Xu and Soha Pouya and Yan Chang},
year = {2026},
eprint = {2607.00033},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.00033},
}
Slow system plans sparse long-horizon intent, fast system refines dense motion, linked by a latent Schrödinger bridge. Reports 45.8% lower latency than Cosmos-Policy and 70.8% success on six bimanual tasks.慢系统生成稀疏的长时程意图，快系统细化高频动作，二者以潜空间薛定谔桥耦合。报告延迟比 Cosmos-Policy 低 45.8%，六个双臂灵巧任务成功率 70.8%。
@misc{chen2026lima,
title = {{LiMA: Bridging Long-term Imagination to Real-time Dexterous Manipulation via Asynchronous Diffusion}},
author = {Ning Chen and Yankai Fu and Junkai Zhao and Qianpu Sun and Guocai Yao and Pengwei Wang and Zhongyuan Wang and Shanghang Zhang},
year = {2026},
eprint = {2609.28431},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.28431},
}
Grasps several objects at once with a dexterous hand by learning one-step residual corrections to a reference trajectory, with a strategy that adapts to how the objects are laid out.让灵巧手一次抓取多个物体：学习对参考轨迹的单步残差修正，并根据物体的空间布局调整抓取策略。
Extends robot piano to one to five Shadow Hands, with robust, safe and multi-agent RL settings: at least 912 task settings and 36 baselines. Reports that top RL algorithms and LLM agents still struggle.把机器人弹钢琴扩展到 1 至 5 只 Shadow 手，并支持鲁棒、安全和多智能体强化学习：至少 912 种任务设置、36 个基线算法。作者报告前沿算法与大模型智能体在高难任务上仍很吃力。
@misc{xu2026omnipiano,
title = {{OmniPiano: Diverse Dexterous Piano-Playing Challenges for Standard, Robust, Safe, and Multi-Agent RL}},
author = {Fan Xu and Fan Yang and Yifan Xu and Ruiyuan Zhang and Yimou Wu and Nuo Yi and Muning Wen and Guang Chen and Costas Spanos and Shangding Gu},
year = {2026},
eprint = {2611.00243},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2611.00243},
}
CoRL 2026 Oral. Generates executable dexterous grasps from free-form language, inferring the function a task implies from multi-view images and geometry, supported by the OpenDexVerse data.CoRL 2026 Oral。根据自由语言生成可执行的灵巧抓取：结合多视角图像与几何推断任务所需的功能，由 OpenDexVerse 数据支撑。
@misc{zhang2026opendexgrasp,
title = {{OpenDexGrasp: Open-vocabulary Task-Oriented Dexterous Grasping}},
author = {Jiyao Zhang and Junhan Wang and Tianyu Wang and Zeyuan Chen and Anthony Bolton and Yitong Peng and Hao Dong},
year = {2026},
eprint = {2609.18117},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.18117},
}
Fully linkage-driven humanoid hand with 3 actuated DoF per finger; a flexure hinge and spring-steel drive rods replace many tiny parts, so it prints on a budget 3D printer at near human-hand size.完全连杆驱动的人形灵巧手，每根手指 3 个主动自由度；用柔性铰链和弹簧钢驱动杆取代大量微型零件，可用入门级 3D 打印机制造，尺寸接近真人手。
@misc{adwani2026phd,
title = {{PHD-Hand: A Printable Humanoid Dexterous Hand with Linkage-Driven 3-DOA Digits}},
author = {Neel Adwani and Muhaiminul Islam Akash and Saffia Ouali and Ethan Liu and Cong Wang},
year = {2026},
eprint = {2610.29030},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29030},
}
Learns tactile dexterous skills without tactile simulation: privileged sensors in the real world collect data to distil a tactile state estimator for a sim-trained policy. Reports 182% gain on in-hand rotation.无需触觉仿真即可学习触觉灵巧操作：在真实环境中借助特权传感器采集数据，蒸馏出基于触觉的状态估计器，用于仿真训练的策略。报告手内旋转任务提升 182%。
@misc{chen2026ptld,
title = {{PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation}},
author = {Rosy Chen and Mustafa Mukadam and Michael Kaess and Tingfan Wu and Francois R Hogan and Jitendra Malik and Akash Sharma},
year = {2026},
eprint = {2603.04531},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2603.04531},
}
CoRL 2026 Oral. Asks what dexterous play pretraining actually needs before precise assembly, a contact-rich, sparse-reward task that is hard for both imitation and direct RL.CoRL 2026 Oral。探究灵巧“玩耍”预训练究竟需要什么，才能胜任精密装配这类富接触、稀疏奖励、模仿与直接 RL 都难以解决的任务。
@misc{lum2026play,
title = {{Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?}},
author = {Tyler Ga Wei Lum and Kushal Kedia and C. Karen Liu and Jeannette Bohg},
year = {2026},
eprint = {2606.26428},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.26428},
}
Uses hand-object proximity from reconstructed point clouds as a hardware-agnostic contact signal, for VR teleop feedback and a policy with learned proximity dynamics. More robust under occlusion.以重建点云得到的手-物距离作为与硬件无关的接触信号，用于 VR 遥操作反馈，并训练带接近度动力学的策略；在遮挡下更稳健。
@misc{bai2026proxidex,
title = {{ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation}},
author = {Yushan Bai and Boyu Zheng and Zhiyang Mao and Hongzheng Sun and Yuchuang Tong and En Li and Zhengtao Zhang},
year = {2026},
eprint = {2609.16586},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.16586},
}
Wearable dexterous UMI: a shared end-effector module (light hand, in-hand camera, fingertip touch) driven by an isomorphic glove, so collected and deployed observations and actions match. Eight real tasks.可穿戴的灵巧 UMI：共享的末端模块（轻量灵巧手、手内相机、指尖触觉）由同构手套驱动，采集与部署时的观测和动作完全一致，无需重定向。在八个真机任务上验证。
@misc{xu2026realdexumi,
title = {{RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning}},
author = {Chaoyi Xu and Yixuan Jiang and Jiahui Huan and Yuhui Fu and Haoyu Zhou and Weitian Yuan and Jiayi Yu and Wanpeng Zhang and Haoqi Yuan and Zongqing Lu},
year = {2026},
eprint = {2606.06033},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.06033},
}
SpatialDex pre-trains a VLM-based dexterous policy on scene geometry and hand motion reconstructed from 1M RGB-only human clips. Reports +38.3 pts over prior methods on 6 real bimanual tasks.SpatialDex 从 100 万段纯 RGB 人类视频中重建场景几何与手部运动，并以几何作为输入和预测目标预训练基于 VLM 的灵巧策略；报告在 9 个仿真任务上比现有方法高 16.5 分、6 个真实双臂灵巧任务上高 38.3 分。
@misc{he2026reconstruction,
title = {{Reconstruction-Augmented Visuomotor Pre-Training of 3D-Grounded Bimanual Dexterity from RGB-Only Human Videos}},
author = {Xingyi He and Zhiwen Qiu and Adhitya Polavaram and Yunhao Cao and Cory Fan and Julius Samwer and Saleh Kalantari and Kuan Fang},
year = {2026},
eprint = {2610.29716},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29716},
}
Tactile simulator for the BrainCo Revo3 hand modelling 285 piezoresistive taxels and five visuotactile fingertips (pressure, depth, RGB, marker motion); up to 4,096 parallel envs on one RTX 5090.为 BrainCo Revo3 灵巧手构建的触觉仿真器，同时模拟 285 个压阻触点和五个视触觉指尖（压力、深度、RGB、标记点位移）；单张 RTX 5090 最多 4096 个并行环境。
@misc{zhang2026revosim,
title = {{RevoSim: Scalable Multimodal Tactile Simulation for Dexterous Manipulation}},
author = {Ke Zhang and Lixin Xu and Ziyi Wang and Xiyue Dong and Jie Tan and Lianghu Shao and Xianpeng Wang and Chuanyu Li and Renjing Xu},
year = {2026},
eprint = {2611.08939},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08939},
}
Human and robot wear the same hand exoskeleton, so encoders and wrist cameras match between collection and rollout; retargeting becomes paired supervision. 3.0x faster collection, 70.0% success on five tasks.人与机器人佩戴同一套手部外骨骼，采集与执行时关节编码器和腕部相机看到相同的机构，重定向变成成对监督。数据采集效率是外骨骼遥操作的 3.0 倍，五个任务平均成功率 70.0%。
@misc{yu2026seed,
title = {{SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration}},
author = {Tengbo Yu and Jiahao Wu and Daohan Li and Bingxu Chen and Hao Liu and Xiaojian Ma and Hangxin Liu},
year = {2026},
eprint = {2609.11753},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.11753},
}
ARPG pretrains an action encoder on human manipulation data, then trains a 130M generative policy in its space. Reports bimanual dexterous success up from 10.1% (π0.5) to 44.7% on DexJoCo.ARPG 把动作表征学习与动作生成解耦：先在大规模人类操作数据上预训练动作编码器，再在其表征空间训练 1.3 亿参数的生成策略。报告在 DexJoCo 双手灵巧任务上平均成功率从 π0.5 的 10.1% 提升到 44.7%。
@misc{xu2026scaling,
title = {{Scaling Dexterous Manipulation via Action Representation Pretraining}},
author = {Yanjinghao Xu and Jiexin Zhang and Huan Zhao and Jianjia Mo and Zhenghao Yin and Dingkang Liang and Xiang Bai and Han Ding},
year = {2026},
eprint = {2610.34453},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34453},
}
One shared object-pose reward, varied training scenes: RL experts discover regrasping, in-hand adjustment and handover on their own, then distil into one policy. Reports 61 of 80 real trials with unseen objects.只用统一的物体位姿奖励并变换训练环境，强化学习专家自行学会重抓、手内调整与双手交接，再蒸馏成单一策略。报告在未见物体上真实实验 80 次成功 61 次。
@misc{yan2026simduoreal,
title = {{SimDuoReal: Object-Centric Bimanual Policy for Emergent Dexterity}},
author = {Qiyang Yan and Yulin Liu and Akash Sharma and Haozhi Qi and Guanya Shi and C. Karen Liu},
year = {2026},
eprint = {2611.10410},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10410},
}
SlipSense fuses a 240 Hz pressure array with an 8 kHz accelerometer for slip detection: 96.7% macro F1, 76% of slips caught within 23.1 ms; trained on UMI data, it transfers to a dexterous hand.SlipSense 融合 240 Hz 压力阵列与 8 kHz 加速度计进行滑移检测：宏 F1 达 96.7%，76% 的滑移在 23.1 毫秒内被检测到；仅用 UMI 数据训练即可零样本迁移到灵巧手。
@misc{jian2026slipsense,
title = {{SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection}},
author = {Tong Jian and Aditya Thurvas Senthil Kumar and Xinyi Li and Ziling Chen and Tianyu Dai and Ali Sengul and Matteo Grimaldi and Wenjie Lu and Saleh Nabi and Tao Yu},
year = {2026},
eprint = {2609.15910},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.15910},
}
Treats human-to-robot transfer as virtual try-on: keeps the motion and swaps the person for a target robot in video; reports novel-task success rising from 2% to 60% with no real demos of those tasks.把人到机器人的迁移看作虚拟试穿：保留动作，把视频中的人换成目标机器人；报告在没有这些任务真机演示时，新任务成功率从 2% 升到 60%。
@misc{lee2026synthesizing,
title = {{Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On}},
author = {Seungyong Lee and Suhyeok Jang and Minyoung Kim and Jinwoo Shin},
year = {2026},
eprint = {2611.05163},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05163},
}
A simulation benchmark of about 0.5B grasp labels over eight dexterous hands and ~50K objects, plus a diffusion model for cross-embodiment grasps in clutter: 88% average success, 0.3 s inference.构建约 5 亿条抓取标注、覆盖 8 种灵巧手和约 5 万个物体的仿真基准，并提出跨本体的杂乱场景灵巧抓取扩散模型：平均成功率 88%，推理 0.3 秒。
@misc{fei2026t,
title = {{T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale}},
author = {Xin Fei and Zhixuan Xu and Huaicong Fang and Fangjun Chen and Zhaoyu Mu and Rui Yang and Zhexi Luo and Lin Shao},
year = {2026},
eprint = {2610.25410},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25410},
}
Tactile-reactive VLA: 100-hour tactile dataset built around motor primitives, a variable-rate mixture-of-transformers and a temporal tactile VQ-VAE. Reports 30%+ higher average success than the best baseline on 12 tasks.触觉反应式 VLA：围绕基本动作采集的 100 小时触觉数据集、可变频率的 Mixture-of-Transformers 架构与时序触觉 VQ-VAE 编码器。报告在 12 个任务上平均成功率比最强基线高 30% 以上。
@misc{niu2026t,
title = {{T-Rex: Tactile-Reactive Dexterous Manipulation}},
author = {Dantong Niu and Zhuoyang Liu and Zekai Wang and Boning Shao and Zhao-Heng Yin and Anirudh Pai and Yuvan Sharma and Stefano Saravalle and Ruijie Zheng and Jing Wang and Ryan Punamiya and Mengda Xu and Yuqi Xie and Yunfan Jiang and Letian Fu and Konstantinos Kallidromitis and Matteo Gioia and Junyi Zhang and Jiaxin Ge and Haiwen Feng and Fabio Galasso and Wei Zhan and David M. Chan and Yutong Bai and Roei Herzig and Jiahui Lei and Li Fei-Fei and Ken Goldberg and Jitendra Malik and Pieter Abbeel and Yuke Zhu and Danfei Xu and Linxi Fan and Trevor Darrell},
year = {2026},
eprint = {2606.17055},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.17055},
}
Touch2Trace feeds a cable through a dexterous hand from touch alone: a self-supervised encoder for a 32x32 piezoresistive sensor plus a small transformer BC policy at 60 Hz; 93% success vs 0% without touch.Touch2Trace 仅凭触觉让灵巧手完成线缆穿引：为 32x32 压阻触觉传感器自监督预训练编码器，配合 60 Hz 的轻量 Transformer 行为克隆策略；成功率 93%，无触觉时为 0%。
@misc{grimaldi2026touch,
title = {{Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing}},
author = {Matteo Grimaldi and David Klee and Ziling Chen and Tong Jian and Wonju Lee and Wenjie Lu and Tao Yu and Saleh Nabi},
year = {2026},
eprint = {2609.15921},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.15921},
}
Teleoperation controller that turns operator intent into learned hand-object contact execution, trained with single-stage RL on human motions. Reports 75% average success on seven in-hand and tool-use tasks.遥操作控制器把操作者意图转化为学习到的手-物接触执行，基于人类动作用单阶段强化学习训练，零样本迁移真机。报告七个手内操作与工具使用任务平均成功率 75%。
@misc{li2026towards,
title = {{Towards Human-level Dexterous Teleoperation}},
author = {Puhao Li and Zeyuan Chen and Yingying Wu and Pengkun Wei and Yuyang Li and Tianyu Wang and Jiaxiao Shi and Mingrui Yu and Baoxiong Jia and Song-chun Zhu and Tengyu Liu and Siyuan Huang},
year = {2026},
eprint = {2607.11481},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.11481},
}
Few-shot calibration of biased mocap gloves or hand trackers: a HyperNet reads a few paired poses and predicts LoRA updates for a frozen MANO estimator, with no per-user fine-tuning.对存在偏差的动捕手套或手部追踪做少样本校准：HyperNet 读取少量成对姿态，为冻结的 MANO 手部估计模块预测 LoRA 式更新，无需逐用户微调。
@misc{zhang2026yoco,
title = {{YOCO: You Only Calibrate Once! Fast Mocap Calibration for Dexterous Teleoperation}},
author = {Yu Zhang and Yunqi Li and Yushi Du and Yi Ma and Yanchao Yang},
year = {2026},
eprint = {2610.11657},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.11657},
}
Finger-driven handheld gripper for UMI-style data collection plus an 8,434-hour, 1.20M-episode, 119-task dataset; one policy transfers across UR, Franka and ELEY by mounting the gripper. Hardware and data released.手指驱动的手持夹爪，用于 UMI 式数据采集，并发布 8434 小时、120 万条、119 个任务的数据集；只需装上同款夹爪，同一策略即可迁移到 UR、Franka 与 ELEY。开源硬件与数据。
@misc{ohkawa2026yubi,
title = {{YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale}},
author = {Takehiko Ohkawa and Jumpei Arima and Yuki Noguchi and Masatoshi Tateno and Makoto Sugiura and Takuya Okubo and Kengo Ikeuchi and Yuma Shin and Hiroki Nishizawa and Naoaki Kanazawa and Yuki Wakayama and Daiki Fukunaga and Koshi Makihara and Tomohiro Motoda and Floris Erich and Yukiyasu Domae and Tatsuya Matsushima and Yohishiro Okumatsu and Kei Ota},
year = {2026},
eprint = {2606.10244},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.10244},
}
AMO joins sim-to-real RL with trajectory optimization for adaptive whole-body control, enabling large-workspace tasks such as picking objects off the ground.AMO 将 sim-to-real 强化学习与轨迹优化结合，实现自适应全身控制，支持从地面拾物等大工作空间任务。
@misc{li2025amo,
title = {{AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control}},
author = {Jialong Li and Xuxin Cheng and Tianshu Huang and Shiqi Yang and Ri-Zhao Qiu and Xiaolong Wang},
year = {2025},
eprint = {2505.03738},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.03738},
}
A dexterous VLA pretrained on human hand motion from mocap, VR and RGB video ('physical instruction tuning'), with part-level motion tokens, then aligned to 3D space and post-trained for real robot hands.灵巧手 VLA：先在动捕、VR 与普通视频中的人手动作上预训练（"物理指令微调"），使用部件级动作 token，再做三维空间对齐，并针对真实机器人灵巧手后训练。
@misc{luo2025being,
title = {{Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos}},
author = {Hao Luo and Yicheng Feng and Wanpeng Zhang and Sipeng Zheng and Ye Wang and Haoqi Yuan and Jiazheng Liu and Chaoyi Xu and Qin Jin and Zongqing Lu},
year = {2025},
eprint = {2507.15597},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2507.15597},
}
An open-source teleoperation glove buildable in hours for under $600, with 21-DoF hand capture, 5-DoF cable-driven force feedback and fingertip haptics; reports the feedback matters for contact-rich tasks without vision.开源遥操作手套，几小时即可组装、成本低于 600 美元：21 自由度手部动捕、5 自由度绳驱力反馈和指尖振动触觉；报告在无视觉输入时力/触觉反馈对接触密集任务至关重要，采集数据可训练模仿学习策略。
@misc{zhang2025doglove,
title = {{DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove}},
author = {Han Zhang and Songbo Hu and Zhecheng Yuan and Huazhe Xu},
year = {2025},
eprint = {2502.07730},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.07730},
}
A pretrained VLM plans and a diffusion controller grasps with a dexterous hand, working on domain-invariant features from foundation models; reports 90%+ grasp success across thousands of unseen cluttered scenes.由预训练 VLM 做高层规划、扩散控制器驱动灵巧手抓取，底层输入为基础模型提取的域不变特征；报告在数千个未见过的杂乱场景中抓取成功率超过 90%。
@misc{zhong2025dexgraspvla,
title = {{DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping}},
author = {Yifan Zhong and Xuchuan Huang and Ruochong Li and Ceyao Zhang and Zhang Chen and Tianrui Guan and Fanlian Zeng and Ka Num Lui and Yuyao Ye and Yitao Liang and Yaodong Yang and Yuanpei Chen},
year = {2025},
eprint = {2502.20900},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.20900},
}
DexUMI — an exoskeleton matched to the robot hand's fingertip workspace, plus inpainting that swaps the human hand for the robot's; reports 86% average success (Inspire, XHand) and ~3.2× faster collection than teleop.DexUMI：按目标机器人手的指尖工作空间设计的可穿戴手部外骨骼，再用视频修补把画面中的人手换成机器人手；论文报告在 Inspire 与 XHand 上平均成功率 86%，采集效率约为遥操作的 3.2 倍。
@misc{xu2025dexumi,
title = {{DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation}},
author = {Mengda Xu and Han Zhang and Yifan Hou and Zhenjia Xu and Linxi Fan and Manuela Veloso and Shuran Song},
year = {2025},
eprint = {2505.21864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.21864},
}
Pairs a 2B Qwen2-VL with a ~1B plug-in diffusion action expert with one head per robot, trained in three stages (cross-embodiment, alignment, task); reports shirt folding without task-specific tuning and 97.3 on LIBERO.将 2B 的 Qwen2-VL 与约 1B 参数、按机器人分头输出的可插拔扩散动作专家结合，分三阶段训练（跨本体预训练、本体对齐、任务适配）；报告无需任务专门微调即可叠衬衫，LIBERO 平均 97.3。
@misc{wen2025dexvla,
title = {{DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control}},
author = {Junjie Wen and Yichen Zhu and Jinming Li and Zhibin Tang and Chaomin Shen and Feifei Feng},
year = {2025},
eprint = {2502.05855},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.05855},
}
829 hours of egocentric Apple Vision Pro video with tracked 3D poses for every hand joint, across 194 tabletop tasks from tying shoelaces to folding laundry; includes hand-trajectory prediction benchmarks.用 Apple Vision Pro 采集的 829 小时第一视角视频，配有每个手部关节的三维姿态，覆盖从系鞋带到叠衣服的 194 种桌面任务，并提供手部轨迹预测基准。
@misc{hoque2025egodex,
title = {{EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video}},
author = {Ryan Hoque and Peide Huang and David J. Yoon and Mouli Sivapurapu and Jian Zhang},
year = {2025},
eprint = {2505.11709},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2505.11709},
}
Compresses action chunks with a DCT-based tokenizer so autoregressive VLAs can learn high-frequency, dexterous control.用基于 DCT 的分词器压缩动作块，使自回归 VLA 能学习高频灵巧控制。
@misc{pertsch2025fast,
title = {{FAST: Efficient Action Tokenization for Vision-Language-Action Models}},
author = {Karl Pertsch and Kyle Stachowicz and Brian Ichter and Danny Driess and Suraj Nair and Quan Vuong and Oier Mees and Chelsea Finn and Sergey Levine},
year = {2025},
eprint = {2501.09747},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2501.09747},
}
@misc{team2025gemini,
title = {{Gemini Robotics: Bringing AI into the Physical World}},
author = {Gemini Robotics Team and Saminda Abeyruwan and Joshua Ainslie and Jean-Baptiste Alayrac and Montserrat Gonzalez Arenas and Travis Armstrong and Ashwin Balakrishna and Robert Baruch and Maria Bauza and Michiel Blokzijl and Steven Bohez and Konstantinos Bousmalis and Anthony Brohan and Thomas Buschmann and Arunkumar Byravan and Serkan Cabi and Ken Caluwaerts and Federico Casarini and Oscar Chang and Jose Enrique Chen and Xi Chen and Hao-Tien Lewis Chiang and Krzysztof Choromanski and David D'Ambrosio and Sudeep Dasari and Todor Davchev and Coline Devin and Norman Di Palo and Tianli Ding and Adil Dostmohamed and Danny Driess and Yilun Du and Debidatta Dwibedi and Michael Elabd and Claudio Fantacci and Cody Fong and Erik Frey and Chuyuan Fu and Marissa Giustina and Keerthana Gopalakrishnan and Laura Graesser and Leonard Hasenclever and Nicolas Heess and Brandon Hernaez and Alexander Herzog and R. Alex Hofer and Jan Humplik and Atil Iscen and Mithun George Jacob and Deepali Jain and others},
year = {2025},
eprint = {2503.20020},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.20020},
}
Procedural dexterous grasp synthesis reporting orders-of-magnitude speedups, without carefully tuned energy functions, including for irregular tool-like objects.过程式灵巧抓取合成，报告提速数个量级，无需精调能量函数，并适用于不规则的工具类物体。
A 3D-printable, 17-DoF tendon-driven hand with fingertip tactile sensing, under CHF 2,000 in materials; design files and code are open, and the paper reports long continuous runs for teleoperation and learning.可 3D 打印的 17 自由度腱驱动灵巧手，指尖带触觉传感，材料成本低于 2000 瑞士法郎；设计文件与代码开源，论文报告了可长时间连续运行用于遥操作与学习。
@misc{christoph2025orca,
title = {{ORCA: An Open-Source, Reliable, Cost-Effective, Anthropomorphic Robotic Hand for Uninterrupted Dexterous Task Learning}},
author = {Clemens C. Christoph and Maximilian Eberlein and Filippos Katsimalis and Arturo Roberti and Aristotelis Sympetheros and Michel R. Vogt and Davide Liconti and Chenyu Yang and Barnabas Gavin Cangan and Ronan J. Hinchet and Robert K. Katzschmann},
year = {2025},
eprint = {2504.04259},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.04259},
}
Picks up paper-like thin objects with a multi-fingered hand carrying high-resolution omnidirectional tactile sensors, tackling state estimation and grasp planning.用带高分辨率全向触觉传感器的多指手抓取纸张类薄物体，同时解决状态估计与抓取规划。
@misc{lin2025pp,
title = {{PP-Tac: Paper Picking Using Tactile Feedback in Dexterous Robotic Hands}},
author = {Pei Lin and Yuzhe Huang and Wanlin Li and Jianpeng Ma and Chenxi Xiao and Ziyuan Jiao},
year = {2025},
eprint = {2504.16649},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.16649},
}
VITRA pretrains VLA models on unscripted in-the-wild human videos, treating the human hand as a dexterous end-effector, with no annotations needed.VITRA 用无脚本的真实人类活动视频预训练 VLA，把人手视为灵巧末端执行器，无需人工标注。
@misc{li2025scalable,
title = {{Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos}},
author = {Qixiu Li and Yu Deng and Yaobo Liang and Lin Luo and Lei Zhou and Chengtang Yao and Lingqi Zeng and Zhiyuan Feng and Huizhi Liang and Sicheng Xu and Yizhong Zhang and Xi Chen and Hao Chen and Lily Sun and Dong Chen and Jiaolong Yang and Baining Guo},
year = {2025},
eprint = {2510.21571},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2510.21571},
}
VPP — fine-tunes Stable Video Diffusion on robot and human videos and feeds its one-pass predictive features to a diffusion policy; reports 4.33 average length on CALVIN ABC→D and real Franka / dexterous-hand results.VPP：在机器人与人类操作视频上微调 Stable Video Diffusion，只做一次前向提取"预测性"视觉特征再交给扩散策略；论文报告 CALVIN ABC→D 平均完成长度 4.33，并有 Franka 与灵巧手真机实验。
@misc{hu2024video,
title = {{Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations}},
author = {Yucheng Hu and Yanjiang Guo and Pengchao Wang and Xiaoyu Chen and Yen-Jen Wang and Jianke Zhang and Koushil Sreenath and Chaochao Lu and Jianyu Chen},
year = {2024},
eprint = {2412.14803},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2412.14803},
}
ACE — a 3D-printed bimanual exoskeleton for wrist pose plus hand-facing webcams for finger pose (about $600); one setup teleoperates arm-hand systems, humanoids and quadruped-mounted arms.ACE：3D 打印的双臂外骨骼获取手腕位姿、朝向手部的摄像头估计手指姿态（约 600 美元）；同一套设备可遥操作机械臂+灵巧手、人形机器人以及四足搭载的机械臂。
@misc{yang2024ace,
title = {{ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation}},
author = {Shiqi Yang and Minghuan Liu and Yuzhe Qin and Runyu Ding and Jialong Li and Xuxin Cheng and Ruihan Yang and Sha Yi and Xiaolong Wang},
year = {2024},
eprint = {2408.11805},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2408.11805},
}
@misc{shaw2024bimanual,
title = {{Bimanual Dexterity for Complex Tasks}},
author = {Kenneth Shaw and Yulong Li and Jiahui Yang and Mohan Kumar Srirama and Ray Liu and Haoyu Xiong and Russell Mendonca and Deepak Pathak},
year = {2024},
eprint = {2411.13677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.13677},
}
Apple Vision Pro teleoperation of two dexterous robot hands with real-time retargeting, collision and singularity avoidance, and low-cost vibration haptics for the operator.用 Apple Vision Pro 实时遥操作双灵巧手，带实时重定向、碰撞与奇异点规避，并为操作者提供低成本振动触觉反馈。
@misc{ding2024bunny,
title = {{Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning}},
author = {Runyu Ding and Yuzhe Qin and Jiyue Zhu and Chengzhe Jia and Shiqi Yang and Ruihan Yang and Xiaojuan Qi and Xiaolong Wang},
year = {2024},
eprint = {2407.03162},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.03162},
}
@misc{wang2024dexcap,
title = {{DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation}},
author = {Chen Wang and Haochen Shi and Weizhuo Wang and Ruohan Zhang and Li Fei-Fei and C. Karen Liu},
year = {2024},
eprint = {2403.07788},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07788},
}
Extends MimicGen to humanoids with two dexterous hands, turning 60 human demos into 21K simulated ones across bimanual tasks, plus a real-to-sim-to-real pipeline tested on real can sorting.把 MimicGen 扩展到双灵巧手人形机器人，从 60 条人类演示生成 2.1 万条双臂仿真演示，并给出在真实分拣罐子任务上验证的 real-to-sim-to-real 流程。
@misc{jiang2024dexmimicgen,
title = {{DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning}},
author = {Zhenyu Jiang and Yuqi Xie and Kevin Lin and Zhenjia Xu and Weikang Wan and Ajay Mandlekar and Linxi Fan and Yuke Zhu},
year = {2024},
eprint = {2410.24185},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24185},
}
27 simulated tasks (12 locomotion, 15 whole-body manipulation) on a Unitree H1 with two dexterous hands; reports that DreamerV3, TD-MPC2, SAC and PPO fail most of them, especially long-horizon whole-body ones.27 个仿真任务（12 个运动、15 个全身操作），机器人为带两只灵巧手的 Unitree H1；报告 DreamerV3、TD-MPC2、SAC、PPO 在大多数任务上未达标，长时程全身协调任务尤甚。
@misc{sferrazza2024humanoidbench,
title = {{HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation}},
author = {Carmelo Sferrazza and Dun-Ming Huang and Xingyu Lin and Youngwoon Lee and Pieter Abbeel},
year = {2024},
eprint = {2403.10506},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.10506},
}
HATO — bimanual setup from two UR5e arms and repurposed prosthetic hands with fingertip touch sensors, VR teleop for data collection, and visuotactile policies for tasks like pouring and handovers.HATO：用两台 UR5e 机械臂和改装的假肢手（指尖带触觉传感器）搭建双臂系统，用 VR 遥操作采集数据，并训练视觉-触觉策略完成倒酒、交接等任务。
@misc{lin2024learning,
title = {{Learning Visuotactile Skills with Two Multifingered Hands}},
author = {Toru Lin and Yu Zhang and Qiyang Li and Haozhi Qi and Brent Yi and Sergey Levine and Jitendra Malik},
year = {2024},
eprint = {2404.16823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2404.16823},
}
An 'ear-in-hand' gripper records audio with vision during human demos in the wild; contact sound then helps policies when vision alone is ambiguous.“手上长耳朵”：夹爪在野外人类演示时同步录制音频与视觉；当视觉信息不足时，接触声帮助策略完成接触密集操作。
@misc{liu2024maniwav,
title = {{ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data}},
author = {Zeyi Liu and Cheng Chi and Eric Cousineau and Naveen Kuppuswamy and Benjamin Burchfiel and Shuran Song},
year = {2024},
eprint = {2406.19464},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.19464},
}
Multi-GraspSet, a large multi-hand grasp dataset with automatic contact annotations, and an LLM that generates language-guided grasps for different robot hands in one model.发布带自动接触标注的大规模多手型抓取数据集 Multi-GraspSet，并用一个基于大模型的统一框架，根据语言指令为不同机器人手生成抓取。
@misc{li2024multi,
title = {{Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation}},
author = {Haosheng Li and Weixin Mao and Weipeng Deng and Chenyu Meng and Haoqiang Fan and Tiancai Wang and Yoshie Osamu and Ping Tan and Hongan Wang and Xiaoming Deng},
year = {2024},
eprint = {2412.08468},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.08468},
}
One RL whole-body controller that a humanoid can follow from a VR headset, verbal commands or an RGB camera, and that also runs learned autonomous policies.一个强化学习全身控制器：人形机器人可跟随 VR 头显、语音指令或 RGB 相机的输入，也可执行学习得到的自主策略。
@misc{he2024omnih,
title = {{OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning}},
author = {Tairan He and Zhengyi Luo and Xialin He and Wenli Xiao and Chong Zhang and Weinan Zhang and Kris Kitani and Changliu Liu and Guanya Shi},
year = {2024},
eprint = {2406.08858},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.08858},
}
Camera-only teleoperation for arm + dexterous hand pairs - tracks the operator's hand and retargets it to many different robot hands, in sim or on real hardware.仅用相机即可遥操作"机械臂 + 灵巧手"：跟踪操作者手部并重定向到多种不同的机器人手，可用于仿真或真机。
@misc{qin2023anyteleop,
title = {{AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System}},
author = {Yuzhe Qin and Wei Yang and Binghao Huang and Karl Van Wyk and Hao Su and Xiaolong Wang and Yu-Wei Chao and Dieter Fox},
year = {2023},
eprint = {2307.04577},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.04577},
}
RotateIt trains in simulation with privileged object information, then distills to a policy that rotates diverse objects in hand using vision, touch and proprioception on a real hand.RotateIt 先在仿真中利用特权物体信息训练，再蒸馏为仅用视觉、触觉和本体感觉的策略，在真实灵巧手上旋转多种物体。
@misc{qi2023general,
title = {{General In-Hand Object Rotation with Vision and Touch}},
author = {Haozhi Qi and Brent Yi and Sudharshan Suresh and Mike Lambeta and Yi Ma and Roberto Calandra and Jitendra Malik},
year = {2023},
eprint = {2309.09979},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.09979},
}
NeuralFeels fuses vision and touch from a multi-fingered hand into an online neural field, reconstructing an object's shape and tracking its pose while the hand manipulates it.NeuralFeels 将多指手的视觉与触觉在线融合为神经场，在操作过程中同时重建物体形状并跟踪其位姿。
@misc{suresh2023neural,
title = {{Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation}},
author = {Sudharshan Suresh and Haozhi Qi and Tingfan Wu and Taosha Fan and Luis Pineda and Mike Lambeta and Jitendra Malik and Mrinal Kalakrishnan and Roberto Calandra and Michael Kaess and Joseph Ortiz and Mustafa Mukadam},
year = {2023},
eprint = {2312.13469},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.13469},
}
Turns tactile readings into points in the same 3D frame as the camera point cloud and the hand model, so one visuotactile point-cloud input drives in-hand object rotation.将触觉读数转换为与相机点云、手部模型处于同一三维坐标系的点，用统一的视触觉点云输入驱动手内物体旋转。
@misc{yuan2023robot,
title = {{Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing}},
author = {Ying Yuan and Haichuan Che and Yuzhe Qin and Binghao Huang and Zhao-Heng Yin and Kang-Won Lee and Yi Wu and Soo-Chul Lim and Xiaolong Wang},
year = {2023},
eprint = {2312.01853},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.01853},
}
Covers a robot hand with cheap binary (contact / no contact) touch sensors and trains an RL policy in simulation that rotates unseen objects in hand on a real robot without any vision.在灵巧手上铺设廉价的二值（有/无接触）触觉传感器，在仿真中用强化学习训练策略，迁移到真机后无需视觉即可在手中旋转未见过的物体。
@misc{yin2023rotating,
title = {{Rotating without Seeing: Towards In-hand Dexterity through Touch}},
author = {Zhao-Heng Yin and Binghao Huang and Yuzhe Qin and Qifeng Chen and Xiaolong Wang},
year = {2023},
eprint = {2303.10880},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.10880},
}
2.1M video frames of two hands manipulating articulated objects (scissors, laptops…) with accurate 3D hand and object meshes from motion capture; a reference for bimanual hand-object reconstruction.210 万帧双手操作铰接物体（剪刀、笔记本电脑等）的视频，借助动作捕捉提供精确的手与物体三维网格；双手-物体重建的常用基准。
@misc{fan2022arctic,
title = {{ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation}},
author = {Zicong Fan and Omid Taheri and Dimitrios Tzionas and Muhammed Kocabas and Manuel Kaufmann and Michael J. Black and Otmar Hilliges},
year = {2022},
eprint = {2204.13662},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2204.13662},
}
Over a million physically validated ShadowHand grasps on thousands of objects, synthesized by optimizing a differentiable force-closure energy; a standard training set for dexterous grasp models.通过优化可微的力封闭能量合成、并经物理仿真验证的百万级 ShadowHand 抓取，覆盖数千个物体，是灵巧抓取模型的常用训练集。
@misc{wang2022dexgraspnet,
title = {{DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation}},
author = {Ruicheng Wang and Jialiang Zhang and Jiayi Chen and Yinzhen Xu and Puhao Li and Tengyu Liu and He Wang},
year = {2022},
eprint = {2210.02697},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2210.02697},
}
Affordance labels for ~1,800 household objects plus 50K intent-oriented hand-object interactions recorded on 100 of them; a common source for dexterous grasp synthesis. Follow-up OakInk2 covers bimanual tasks.为约 1800 个日常物体标注可供性，并在其中 100 个物体上记录 5 万次带意图的手-物交互；常用于灵巧抓取生成。后续 OakInk2 扩展到双手任务。
@misc{yang2022oakink,
title = {{OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction}},
author = {Lixin Yang and Kailin Li and Xinyu Zhan and Fei Wu and Anran Xu and Liu Liu and Cewu Lu},
year = {2022},
eprint = {2203.15709},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2203.15709},
}
@misc{chen2022visual,
title = {{Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes}},
author = {Tao Chen and Megha Tippur and Siyang Wu and Vikash Kumar and Edward Adelson and Pulkit Agrawal},
year = {2022},
eprint = {2211.11744},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2211.11744},
}
@misc{chao2021dexycb,
title = {{DexYCB: A Benchmark for Capturing Hand Grasping of Objects}},
author = {Yu-Wei Chao and Wei Yang and Yu Xiang and Pavlo Molchanov and Ankur Handa and Jonathan Tremblay and Yashraj S. Narang and Karl Van Wyk and Umar Iqbal and Stan Birchfield and Jan Kautz and Dieter Fox},
year = {2021},
eprint = {2104.04631},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2104.04631},
}
Advantage-weighted actor-critic that pre-trains on prior demonstrations and keeps improving online, shown on real dexterous-hand tasks.优势加权的 Actor-Critic：先用已有示范数据预训练，再在线持续提升，并在真实灵巧手任务上验证。
@misc{openai2019solving,
title = {{Solving Rubik's Cube with a Robot Hand}},
author = {OpenAI and Ilge Akkaya and Marcin Andrychowicz and Maciek Chociej and Mateusz Litwin and Bob McGrew and Arthur Petron and Alex Paino and Matthias Plappert and Glenn Powell and Raphael Ribas and Jonas Schneider and Nikolas Tezak and Jerry Tworek and Peter Welinder and Lilian Weng and Qiming Yuan and Wojciech Zaremba and Lei Zhang},
year = {2019},
eprint = {1910.07113},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1910.07113},
}
A Shadow Hand reorients a cube using RL trained purely in randomized simulation — a sim-to-real milestone.完全在随机化仿真中用强化学习训练，Shadow 灵巧手在真实世界翻转方块，是 sim-to-real 里程碑。
@misc{openai2018learning,
title = {{Learning Dexterous In-Hand Manipulation}},
author = {OpenAI and Marcin Andrychowicz and Bowen Baker and Maciek Chociej and Rafal Jozefowicz and Bob McGrew and Jakub Pachocki and Arthur Petron and Matthias Plappert and Glenn Powell and Alex Ray and Jonas Schneider and Szymon Sidor and Josh Tobin and Peter Welinder and Lilian Weng and Wojciech Zaremba},
year = {2018},
eprint = {1808.00177},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1808.00177},
}
Bing Han, Yichuan Peng, Haoyu Dong, Yuanmeng Hu et al.
Abstract摘要
Tactile sensing is an indispensable modality for contact-rich dexterous manipulation, a capability that hints at the ultimate frontiers of embodied intelligence. However, commercially available tactile sensors are still at a relatively early stage of development, with each type of sensor offering irreplaceable strengths alongside its own drawbacks. A key challenge is thus to free tactile understanding from sensor-specific physics, so that embodied models can effectively leverage heterogeneous tactile data. To this end, we develop LingBot-Touch, a tactile foundation model characterized by the …
@misc{han2026learning,
title = {{Learning Heterogeneous Tactile Representation for General Dexterous Manipulation}},
author = {Bing Han and Yichuan Peng and Haoyu Dong and Yuanmeng Hu and Yongyi Jia and Fangjing Wang and Kecheng Zheng and Yiming Zhang and others},
year = {2026},
eprint = {2611.12806},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12806},
}
Ruilin Zhang, Xuanshuo Liu, Junzhe Dai, Kefei Wu et al.
Abstract摘要
Modern visuotactile sensors primarily sense contact by imaging the deformation of a compliant interface. Using techniques such as photometric stereo, they can recover detailed contact geometry, often represented as surface normals or depth. However, force-related information typically still needs to be inferred indirectly through calibration, geometric reconstruction, or learned mappings. Mechanochromic photonic-crystal (PC) materials offer an alternative physical encoding mechanism: contact loading changes their periodic microstructure and spectral response, directly transforming local …
@misc{zhang2026pctac,
title = {{PCTac: Mechanochromic Photonic-Crystal Tactile Sensor for Dexterous Manipulation}},
author = {Ruilin Zhang and Xuanshuo Liu and Junzhe Dai and Kefei Wu and Xuhao Qin and Ziyuan Jiao and Wanlin Li and Yurui Qu and others},
year = {2026},
eprint = {2611.12383},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12383},
}
Xianlong Mai, Rui Yu, Rui Chen, Daniele Leonardis et al.
Abstract摘要
Hand kinesthetic feedback is crucial for restoring and enhancing human perception of force and displacement in robotic hand teleoperation. Wide perception range of force feedback is vital in rendering objects of different stiffness, especially for high-stiffness rendering. However, existing approaches struggle to achieve wide range of force feedback while maintaining compact and wearable design. This paper presents a hand exoskeleton system employing magnetorheological (MR) brakes to provide a wide range kinesthetic feedback during teleoperation. The MR brake provides a braking torque ranging …
@misc{mai2026compact,
title = {{Compact and Wide-Force-Range Magnetorheological Hand Exoskeleton for Kinesthetic Feedback in Teleoperation}},
author = {Xianlong Mai and Rui Yu and Rui Chen and Daniele Leonardis and Wenbo Li and Domenico Chiaradia and Antonio Frisoli and Shuaishuai Sun},
year = {2026},
eprint = {2611.12130},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12130},
}
Jinyi Chen, Hongyi Li, Lei Jiang, Xianwei Liu et al.
Abstract摘要
Dexterous hands enable versatile manipulation, but policies trained in simulation often degrade on hardware owing to non-idealities, and the high-dimensional action space makes real-world adaptation costly. Piano playing, which requires precise and coordinated multifinger motion, offers a representative testbed. We propose GAP (Generalized Adaptation Prior for Dexterous Piano Playing), a sample-efficient adaptation framework that learns how to adapt, rather than how to act, during simulation pretraining and transfers this adaptation prior to hardware. Between repeated executions, GAP uses …
@misc{chen2026gap,
title = {{GAP: Generalized Adaptation Prior for Dexterous Piano Playing}},
author = {Jinyi Chen and Hongyi Li and Lei Jiang and Xianwei Liu and Yanhong Liang and Yulin Chen and Yucheng Tao and Yongbin Jin and others},
year = {2026},
eprint = {2611.12111},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12111},
}
Rumi Loghmani, Abraham George, Amir Barati Farimani
Abstract摘要
Dexterous grasping in robotics relies on distinct hand configurations depending on object geometry. We introduce BetaGrasp, a 3D diffusion policy that conditions a DP3-style point-cloud policy on a four-class grasp label (crimp, sloper, pinch, and jug), running on a Franka arm with a 16-DoF LEAP hand. We also introduce a climbing-hold benchmark: a spring testbed whose ratchet gives a slip-force reading in newtons. In our analysis the policy's point-cloud encoder did not separate the four holds, so the scan alone gave it little to choose a grasp from. Without the label (a DP3-style baseline), …
@misc{loghmani2026grasp,
title = {{Grasp-Taxonomy Conditioning as a Mode Switch for Dexterous Grasping of Climbing Holds}},
author = {Rumi Loghmani and Abraham George and Amir Barati Farimani},
year = {2026},
eprint = {2611.12017},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12017},
}
Hyojae Kang, Giup Seo, Jae-Young Lee, Yeong Jae Park et al.
Abstract摘要
Dexterous robotic fingers require coordinated joint motion and adaptability to object contact, while independently actuating multiple joints increases the complexity of the transmission mechanism. This paper presents a hybrid tendon--linkage robotic finger with four mechanical degrees of freedom driven by three actuators. The MCP abduction/adduction joint is driven through a bevel gear, while MCP and PIP flexion/extension are driven by separate bidirectional tendon transmissions. The DIP joint is not independently actuated but is coupled to the PIP joint through a four-bar linkage with a …
@misc{kang2026design,
title = {{Design of a hybrid tendon-linkage finger with coupled and adaptive motion}},
author = {Hyojae Kang and Giup Seo and Jae-Young Lee and Yeong Jae Park and Jongwoo Park and Hyunmin Do and So-Hee Park and Dongil Park and others},
year = {2026},
eprint = {2611.11885},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11885},
}
Bingzhang Wang, Nathan Zhao, Jialeng Ni, Jiajin Zheng et al.
Abstract摘要
Vision-language-action (VLA) models pair a vision-language backbone with an action head, but in closed-loop control the backbone sits on the control-critical path: while it computes, the robot keeps moving, so each executed action is conditioned on an increasingly stale observation. A larger backbone therefore directly blocks reactivity, especially in humanoid dexterous manipulation. We present DASH, a Decoupled Asynchronous System for Humanoids that removes this coupling. DASH splits the policy into a slow vision-language model (System 2) and a fast flow-matching action head (System 1) that …
@misc{wang2026decoupled,
title = {{Decoupled Asynchronous System for Humanoids (DASH): Latency-Agnostic Scaling of Vision-Language-Action Models for Humanoid Manipulation}},
author = {Bingzhang Wang and Nathan Zhao and Jialeng Ni and Jiajin Zheng and Yin Zhang and Kehua Chen and Jian Yao},
year = {2026},
eprint = {2611.11854},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11854},
}
Zhongxi Chen, Yi Yang, Xinyi Chen, Mingzhe Ye et al.
Abstract摘要
Dexterous manipulation requires rich spatial foresight over both the surrounding scene and the robot hand. Existing world-action models often acquire such predictive knowledge through explicit future prediction. We introduce DexForesight, a framework that learns spatial foresight implicitly by injecting dynamics-aware world representations into the latent space of a dexterous policy. During training, DexForesight learns an action-conditioned spatial world model from offline scene depth, kinematic hand geometry, and demonstration trajectories. The world model predicts 3D hand-point motion and …
@misc{chen2026dexforesight,
title = {{DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight}},
author = {Zhongxi Chen and Yi Yang and Xinyi Chen and Mingzhe Ye and Yaping Li and Zhihong Liu and Congsheng Xu and Haonan Wang and others},
year = {2026},
eprint = {2611.11166},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11166},
}
Dong Fang, Feixuan Huang, Yuanxin Zhong, Yuxiang Gao et al.
Abstract摘要
Dexterous manipulation with world action models (WAMs) relies on hand configuration and contact information, yet joint-state and whole-hand tactile observations may be partially or entirely missing. RGB images provide scene context and hand-object geometry but are insufficient to reliably recover these missing modalities. We further observe a correlation between hand state and tactile information. Motivated by this observation, we introduce BAT-WAM, a framework that bridges visual, joint-state, and tactile representations for WAMs. BAT-WAM learns separate codebooks and decoders for the …
@misc{fang2026bat,
title = {{BAT-WAM: Bridging Action and Touch in World-Action Models for Dexterous Hands}},
author = {Dong Fang and Feixuan Huang and Yuanxin Zhong and Yuxiang Gao and Qi Song and Yunlong Wang and Xiaosong Jia and Yu-Gang Jiang},
year = {2026},
eprint = {2611.10967},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10967},
}
Taeyun Kim, Jinsoo Shin, Taehwa Kim, Taeyoon Kim et al.
Abstract摘要
Policies for dexterous manipulation depend on design choices made before training: which sensors enter the observation, where they are placed, and how their readings are arranged for the encoder. These choices are rarely examined. For tactile sensing, simulation studies place taxels uniformly over the hand, and this placement has become a default without being tested. We test this default under a single controlled protocol. The protocol holds the teacher, the training procedure, and the candidate taxels fixed and varies only the sensor configuration and the representation. We evaluate it on …
@misc{kim2026which,
title = {{Which Taxels, Not How Many: Tactile Sensor Placement for Dexterous Policies}},
author = {Taeyun Kim and Jinsoo Shin and Taehwa Kim and Taeyoon Kim and Jinyoung Choi},
year = {2026},
eprint = {2611.10647},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10647},
}
Rongqian Chen, Yiding Chen, Jijie Zhou, Tom Z. Jiahao
Abstract摘要
Most robotic handwriting rigidly mounts the pen and reduces writing to trajectory tracking. A freely held pen instead couples grasp stability, tip motion, and contact force. We present Grip, Trace, Write, a force-aware simulation environment and learning framework for high-DoF handwriting. A two-branch actor separates pen retention from arm-wrist writing while preserving their closed-loop coupling. Training learns a grasp controller, initializes writing from inverse-kinematics demonstrations by behavior cloning, and improves writing through reinforcement learning with rewards on ink, force, …
@misc{chen2026grip,
title = {{Grip, Trace, Write: Force-Aware Imitation and Reinforcement Learning for High-DoF Robotic Handwriting}},
author = {Rongqian Chen and Yiding Chen and Jijie Zhou and Tom Z. Jiahao},
year = {2026},
eprint = {2611.10451},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10451},
}
Qiyang Yan, Yulin Liu, Akash Sharma, Haozhi Qi et al.
Abstract摘要
Bimanual dexterous manipulation requires coordinated arm and hand control, yet specifying such coordination manually is challenging and often task-specific. In this work, we explore whether complex bimanual dexterity can emerge from a simple object-centric learning objective. We train reinforcement learning policies under a shared object-centric reward, varying their training environments rather than tuning separate rewards for each behavior. We find that the policies discover pre-grasp manipulation, grasping and regrasping, in-hand adjustment and handover to reach object-pose goals without …
@misc{yan2026simduoreal,
title = {{SimDuoReal: Object-Centric Bimanual Policy for Emergent Dexterity}},
author = {Qiyang Yan and Yulin Liu and Akash Sharma and Haozhi Qi and Guanya Shi and C. Karen Liu},
year = {2026},
eprint = {2611.10410},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10410},
}
Aryaman Gupta, Xiaoyue Wu, Rana Soltani Zarrin, Mark Van der Merwe
Abstract摘要
Generative robot policies based on diffusion and flow matching can model complex, multimodal behaviors, but remain susceptible to covariate shift during closed-loop execution. Existing approaches mitigate such failures through additional demonstrations, policy fine-tuning, or test-time guidance, but often require human intervention, engineered rewards, or other task-specific signals. We introduce GUARD, an anomaly-informed Q-steering framework that leverages the policy's training distribution to construct a dense, failure-anticipatory steering signal. GUARD pairs sparse task rewards with …
@misc{gupta2026guard,
title = {{GUARD: Anomaly-Informed Q-Steering for Generative Robot Policies in Dexterous Manipulation}},
author = {Aryaman Gupta and Xiaoyue Wu and Rana Soltani Zarrin and Mark Van der Merwe},
year = {2026},
eprint = {2611.10343},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10343},
}
Romina Adhami, Ebrahim Shahabi, Alessio Mondini, Samuel M. Youssef et al.
Abstract摘要
Robotic handling of delicate fruit benefits from compliant contact and quantitative sensing of the mechanical interaction at the end-effector. This study investigates an optoelectronic soft suction cup with a kiwifruit-specific bicomponent design for estimating interaction forces during contact with real fruit. During known-contact intervals, the optical signals accurately estimate interaction force on fruits excluded from model training: the best shallow network achieves grouped out-of-fold pooled R-squared = 0.968 +/- 0.005 (MAE = 0.138 N), with a median per-fruit held-out R-squared = 0.973 …
@misc{adhami2026mechanical,
title = {{Mechanical Interaction Sensing for Robotic Kiwifruit Handling with an Optoelectronic Soft Suction Cup}},
author = {Romina Adhami and Ebrahim Shahabi and Alessio Mondini and Samuel M. Youssef and Cosimo Della Santina and Gualtiero Volpe and Barbara Mazzolai},
year = {2026},
eprint = {2611.09468},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09468},
}
We present RevoSim, a scalable and high-fidelity multimodal tactile simulator for the BrainCo Revo3 hand that jointly models 285 piezoresistive taxels and five visuotactile fingertips. RevoSim efficiently generates synchronized pressure, depth, RGB, and marker-displacement observations; on a single RTX 5090, its depth-conditioned marker-displacement configuration supports up to 4,096 parallel environments. We train in-hand manipulation and grasping policies entirely in simulation, establishing a foundation for future sim-to-real transfer. Project page: …
@misc{zhang2026revosim,
title = {{RevoSim: Scalable Multimodal Tactile Simulation for Dexterous Manipulation}},
author = {Ke Zhang and Lixin Xu and Ziyi Wang and Xiyue Dong and Jie Tan and Lianghu Shao and Xianpeng Wang and Chuanyu Li and others},
year = {2026},
eprint = {2611.08939},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08939},
}
Yixiao Wang, Peiyi Chen, Mustafa Mukadam, Shreekant Gayaka et al.
Abstract摘要
Learning dexterous manipulation from limited visual demonstrations is challenging because policies must acquire both perceptual grounding and precise control, while reinforcement learning (RL) in simulation can acquire rich control behaviors from large-scale interaction with privileged information. We introduce \textbf{Latent RL Prior}, a two-stage framework that reuses this control knowledge for data-efficient visual policy learning. During privileged pretraining, we distill large-scale RL rollouts into a flow-matching action decoder conditioned on proprioception and latent task context. …
@misc{wang2026latent,
title = {{Latent RL Priors for Data-Efficient Dexterous Manipulation}},
author = {Yixiao Wang and Peiyi Chen and Mustafa Mukadam and Shreekant Gayaka and Masayoshi Tomizuka and Son Tran},
year = {2026},
eprint = {2611.08458},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08458},
}
Yijiong Lin, Wenhao He, Toby Jack, Max Yang et al.
Abstract摘要
Robotic simulators that represent shear within tactile sensing are becoming increasingly important for contact-rich manipulation. However, simulating contact shear with high-fidelity deformable or soft-body simulation is computationally demanding, limiting scalability to large-scale reinforcement learning. Here we introduce \textit{TactileLab}, a scalable multimodal tactile simulation framework built on IsaacLab, with a particular focus on a new optical-flow-based tactile representation termed \textit{simulated tactile flow}. Instead of relying on soft-body physics to model shear deformation, …
@misc{lin2026tactilelab,
title = {{TactileLab: Sim-to-Real Dexterous Robot Manipulation with Efficient Shear-Sensitive Tactile Simulation}},
author = {Yijiong Lin and Wenhao He and Toby Jack and Max Yang and Haoran Li and Nathan F. Lepora},
year = {2026},
eprint = {2611.07881},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07881},
}
Nikita Kuzmin, Georgii Demianchuk, Mariya Lezina, Yara Mahmoud et al.
Abstract摘要
Social touch interactions, such as handshakes, fist bumps, and hugs, require humanoid robots to infer human intent, generate contact-rich motions, and maintain safe physical contact. We present AffectiveVLA, an intent-gated Vision-Language-Action (VLA) pipeline deployed on a Unitree G1 humanoid with dexterous hands. A lightweight RGB-based model recognizes interaction intent from two onboard cameras and converts it into a language prompt for the VLA policy. Low-confidence predictions suppress motion execution, reducing unintended activations. During physical contact, interaction-specific …
@misc{kuzmin2026affectivevla,
title = {{AffectiveVLA: Safe and Intent-Aware VLA Control for Human-Humanoid Social Interaction}},
author = {Nikita Kuzmin and Georgii Demianchuk and Mariya Lezina and Yara Mahmoud and Iana Zhura and Miguel Altamirano Cabrera and Dzmitry Tsetserukou},
year = {2026},
eprint = {2611.07712},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07712},
}
Dexterous robotic hands are increasingly coupled to large-scale human data and foundation models, yet progress in hardware, representation learning and evaluation is often assessed separately. This survey reviews work from January 2024 to early November 2026, with earlier studies included to establish technical lineages. We organize the literature into five interdependent layers: hardware; data collection and curation; cross-embodiment transfer; learning and foundation models; and evaluation and applications. Human hand-object understanding and tactile sensing provide two cross-cutting …
Manipulating diverse objects with multi-fingered dexterous hands is challenging due to the high dimensionality and complex dynamics. Human-Object Interaction (HOI) datasets provide rich knowledge about task information and embodied interactions. Instead of solely imitating the human demonstrations, our method learns to holistically predict future hand-object states by leveraging these datasets. The predicted future states of the object can serve as a general-purpose reward term for reinforcement learning, reducing reliance on task-specific reward engineering and enhancing generalization …
Prashant Kumar, Rachael Granberry, Taylor Niehues, Andrew A. Stanley et al.
Abstract摘要
Humanoid robotic hands must provide sufficient force, speed, and backdrivability within a human-scale envelope. Tendon-driven transmissions address this challenge by relocating actuators to the palm and forearm, but routing paths, moment arms, and capstan dimensions jointly determine both force capability and joint speed, so selecting them sequentially satisfies one requirement at the expense of the other. This paper presents a pose-robust actuation co-design for tendon-driven robot hands (PACT) framework that optimizes tendon routing, moment arms, and capstan dimensions under pose-dependent …
@misc{kumar2026pact,
title = {{PACT: Pose-Robust Actuation Co-Design for Tendon-Driven Robotic Hands}},
author = {Prashant Kumar and Rachael Granberry and Taylor Niehues and Andrew A. Stanley and Gabe Pirie},
year = {2026},
eprint = {2611.05302},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05302},
}
Seungyong Lee, Suhyeok Jang, Minyoung Kim, Jinwoo Shin
Abstract摘要
Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations. Egocentric human videos offer a scalable substitute, but overlaying rendered robots on human videos or retargeting human poses into robot actions leaves significant visual or action gaps for high-DoF robot embodiments. To address this limitation, we propose RoboTryOn, which treats human-to-robot translation as virtual try-on: try-on keeps the person and swaps the garment, RoboTryOn keeps the demonstrated motion and swaps the human for …
@misc{lee2026robotryon,
title = {{RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On}},
author = {Seungyong Lee and Suhyeok Jang and Minyoung Kim and Jinwoo Shin},
year = {2026},
eprint = {2611.05163},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05163},
}
Zhewei Gui, Anthony Bolton, Jiyao Zhang, Zeyuan Chen et al.
Abstract摘要
Dexterous manipulation involves not only moving objects, but also arranging the hand around them to maintain useful contacts and enable subsequent actions. Yet many learning-based systems specify goals primarily through object poses or trajectories, leaving hand-object configurations to emerge implicitly. We introduce DexHOT, which formulates dexterous manipulation as hand-object configuration transfer: moving between feasible configurations that jointly specify hand articulation and object pose relative to the wrist. DexHOT learns a reusable relation-conditioned transfer primitive from …
@misc{gui2026dexhot,
title = {{DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation}},
author = {Zhewei Gui and Anthony Bolton and Jiyao Zhang and Zeyuan Chen and Qiyang Yan and Tianyu Wang and Hao Dong},
year = {2026},
eprint = {2611.05119},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05119},
}
Eric T. Chang, Bianca Aumann, Zhanpeng He, Mohamed Elnoor et al.
Abstract摘要
Pre-touch sensing, or sensing information about an object before contact, is an attractive modality for robot hands as it can allow a robot to anticipate and respond to a contact before it happens. In this work, we introduce ToFTouch, a multimodal tactile sensor combining three multizone (4$\times$4 resolution) time-of-flight sensors with an optics-based pressure sensing array. We mount this sensor on a parallel jaw gripper and demonstrate its utility on three distinct tasks: (i) pre-grasp adjustment, (ii) item extraction from clutter, and (iii) item insertion into tight spaces. For pre-grasp …
@misc{chang2026exploring,
title = {{Exploring Proximity and Tactile Sensing for Robotic Manipulation}},
author = {Eric T. Chang and Bianca Aumann and Zhanpeng He and Mohamed Elnoor and Thomas Liang and Matei Ciocarlie and Nima Fazeli and Joshua Migdal and others},
year = {2026},
eprint = {2611.03489},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03489},
}
Markerless hand-eye calibration under continuous motion requires jointly resolving camera geometry and the time offset between images and joint states. We present SyncHeC (Synchronized Hand-Eye Calibration), a dense differentiable-rendering framework that estimates both from a single robot trajectory. Continuous-time forward kinematics makes latency differentiable, metric depth complements mask contours, and an offline planner combines spatial sensitivity with temporal excitation. Alternating updates support Eye-to-Hand, Eye-in-Hand, and multi-camera calibration. On a 7-DoF arm, one $6.5$\,s …
Haonan Ping, Jianshu Hu, Yimin Wang, Yi Zhu et al.
Abstract摘要
Long-horizon dexterous manipulation remains a fundamental challenge for robot learning, requiring the composition of multiple skills, precise bimanual coordination, generalizable visual understanding, and robust skill transitions. Yet existing manipulation benchmarks largely emphasize isolated skills or support only a limited range of end-effectors, making these capabilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 …
@misc{ping2026dexarena,
title = {{DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation}},
author = {Haonan Ping and Jianshu Hu and Yimin Wang and Yi Zhu and Pai Liang and Qiyan Jin and Qirui Bao and Ying Qian and others},
year = {2026},
eprint = {2611.02629},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02629},
}
Arjun Bhardwaj, Mayank Mittal, Maximum Wilder-Smith, Vaishakh Patil et al.
Abstract摘要
Dexterous in-hand manipulation is commonly formulated around explicit object pose estimation, yet reliable pose tracking is challenging under severe hand-object occlusions and often requires object-specific supervision. This dependence creates a bottleneck between perception and control that can limit robustness and scalability. We present an end-to-end visuomotor framework for dexterous in-hand reorientation that operates on RGB observations and proprioception without intermediate pose estimation. Our approach jointly learns visual representations and control policies through behavior …
@misc{bhardwaj2026goaldex,
title = {{GoalDex: Learning Image-Goal-Conditioned Policies for Dexterous In-Hand Manipulation}},
author = {Arjun Bhardwaj and Mayank Mittal and Maximum Wilder-Smith and Vaishakh Patil and Marco Hutter},
year = {2026},
eprint = {2611.02171},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02171},
}
Yiming Chen, Xiaofei Wei, Chun Gu, Wei Xiao et al.
Abstract摘要
Tactile sensing plays a crucial role in everyday human interaction. Consider inserting a plug into a tight socket, vision alone provides limited guidance during the final insertion stage, and we instead rely on both anticipated contact and realized tactile feedback to ajust the insertion motion. The same capability is equally important for robots, particularly in dexterous manipulation tasks. Yet many contemporary robot policies remain vision-dominant. This visual bias, compounded by limited tactile data and sparse contact events, can cause directly incorporated tactile signals to be ignored …
@misc{chen2026tf,
title = {{$TF^2$-Steering: Action Refinement with Tactile Forecasts and Feedback via Velocity-Field Steering}},
author = {Yiming Chen and Xiaofei Wei and Chun Gu and Wei Xiao and Peidong Liu and Li Zhang},
year = {2026},
eprint = {2611.01483},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.01483},
}
Dexterous robotic manipulation remains a major challenge in robotics, exemplified by piano playing, which requires coordinated control of fingers and provides a demanding testbed for reinforcement learning (RL). Although previous work established a benchmark for robotic piano playing, it is restricted to two hands and does not support unified evaluation. To address this gap, we introduce OmniPiano, a benchmark that provides one to up to five Shadow Hands and supports standard, robust, safe, and multi-agent RL within a shared piano-playing task family. OmniPiano is developed for scalable and …
@misc{xu2026omnipiano,
title = {{OmniPiano: Diverse Dexterous Piano-Playing Challenges for Standard, Robust, Safe, and Multi-Agent RL}},
author = {Fan Xu and Fan Yang and Yifan Xu and Ruiyuan Zhang and Yimou Wu and Nuo Yi and Muning Wen and Guang Chen and others},
year = {2026},
eprint = {2611.00243},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2611.00243},
}
Zihan Gao, Yufei Liu, Yuhong Zhang, Zhen Yu et al.
Abstract摘要
Learning dexterous manipulation from human videos remains an open challenge, particularly in the context of transferring a single demonstration into a spatially generalizable robotic skill and maintaining physically plausible human-like interactions. Unlike prior works that emphasize hand-motion imitation, we introduce an object-centric framework for acquiring dexterous skills from egocentric human RGB-D videos. Within our framework, we formulate the reinforcement learning (RL) objective in terms of object motion to decouple the underlying skill from the specific hand trajectory and impose …
@misc{gao2026handmimicx,
title = {{HandMimicX: Learning Human-Like Dexterous Manipulation via an Object-Centric Policy from Egocentric Videos}},
author = {Zihan Gao and Yufei Liu and Yuhong Zhang and Zhen Yu and Xinhao Huang and Zhinan You and Kai W. Ho and Zipeng Ye and others},
year = {2026},
eprint = {2610.41336},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41336},
}
Zhixuan Xu, Yichen Li, Bingchen Wu, Rui Wang et al.
Abstract摘要
Robot-free human demonstrations offer a promising route to scaling dexterous manipulation data. The key challenge is to preserve the demonstrator's natural dexterity while producing contact-grounded observations and actions that transfer to robot hands. We present Contact Imitation Interface (CIMI), a framework with contact-grounded demonstration and learning interfaces. The demonstration interface combines shared contact geometry and fingertip RGB views with contact-alignment-first retargeting and reprojection of wider scene views. The learning interface introduces a hierarchical policy in …
@misc{xu2026contact,
title = {{Contact Imitation Interface: Transferring Dexterous Skills by Showing How Contact Unfolds}},
author = {Zhixuan Xu and Yichen Li and Bingchen Wu and Rui Wang and Yiwen Hou and Zixuan Liu and Tianyu Qiu and Lin Shao},
year = {2026},
eprint = {2610.41028},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41028},
}
Ammar N. Abbas, Aayush Jain, Prashanth Viswanath, Nivesh Gadipudi et al.
Abstract摘要
Imitation learning policies are intended to make robotic manipulation robust to variations in object pose, appearance, and environment that challenge pre-programmed motion. However, real-world performance remains difficult to predict, as conventional optimization signals such as validation loss show no consistent relationship with robot success. To identify the design choices that govern real-world performance, 281 imitation-learning policies spanning seven policy families were evaluated in approximately 1,700 real-robot trials of RJ45 connector grasping and insertion using a five-fingered …
@misc{abbas2026from,
title = {{From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation}},
author = {Ammar N. Abbas and Aayush Jain and Prashanth Viswanath and Nivesh Gadipudi and Giulia Vilone and Jim Gibbons},
year = {2026},
eprint = {2610.40828},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40828},
}
Haozhe Zhang, Gu Zhang, Zirui Zou, Yuzheng Chen et al.
Abstract摘要
Human demonstrations offer an efficient and scalable source of data for dexterous robot manipulation, but differences in sensing and hand morphology complicate the transfer of contact-rich interactions. We present TouchBridge, a hardware--software co-designed framework for human-to-robot data transformation. A wearable human demonstration interface and a corresponding robot interface establish cross-embodiment correspondence in visual and fingertip tactile sensing. We introduce Tac-Retarget, a tactile-conditioned retargeting method that uses measured contact to align fingertip contact patches …
@misc{zhang2026touchbridge,
title = {{TouchBridge: Tactile-Conditioned Human-to-Robot Transfer for Dexterous Manipulation}},
author = {Haozhe Zhang and Gu Zhang and Zirui Zou and Yuzheng Chen and Jinlong Wu and Zixuan Hu and Jianke Zhang and Yucheng Hu and others},
year = {2026},
eprint = {2610.40349},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40349},
}
Bimanual dexterous manipulation requires continuous coordination of wrist motion, finger contact, and object motion. Human videos provide rich manipulation experience. Using this experience for robot control requires aligning human poses with robot commands and accounting for embodiment-specific kinematics and contact dynamics. We present AcrossDEX 1.0, a learning system that connects human motion pretraining, simulation-based expert learning, robot demonstration collection, and visual closed-loop execution. Pretraining on HOT3D Aria predicts one second of bimanual motion from five images and …
@misc{yang2026acrossdex,
title = {{AcrossDEX 1.0: From Human Motion Pretraining to Bimanual Dexterous Manipulation across Hand Embodiments}},
author = {Xiaoyu Yang and Nan Wu},
year = {2026},
eprint = {2610.40210},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40210},
}
Xinyuan He, Ziheng Xu, Yueyuan Chen, Yufan Ye et al.
Abstract摘要
Human demonstrations provide a natural and scalable source of data for learning dexterous manipulation policies. However, existing retargeting methods primarily transfer human kinematics, while the tactile information underlying physical interaction is rarely preserved across embodiments. To address this limitation, we present a hierarchical three-stage framework for kinematic-tactile aligned dexterous retargeting. We use object-centric contact wrench space as a physical representation for tactile alignment between human and robot hands. The framework first aligns hand kinematics and contact …
@misc{he2026kinematic,
title = {{Kinematic-Tactile Aligned Dexterous Retargeting with Contact Wrench Space Alignment}},
author = {Xinyuan He and Ziheng Xu and Yueyuan Chen and Yufan Ye and Yuanshuo Tan and Huiming Pan and Jianjun Meng and Shuo Jiang and others},
year = {2026},
eprint = {2610.39582},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39582},
}
Meng Wang, Jindou Jia, Sicheng Zhou, Zeshuai Chen et al.
Abstract摘要
As a representative form of embodied robotic interaction, aerial grasping requires close coordination between the aerial platform and manipulator. Most existing systems perform aerial grasping with static or slowly moving targets under fixed scenarios. Dynamic and precise grasping across different conditions introduces additional challenges, including target motion, localization drift, dynamic coupling, and external disturbances. In this work, we present CAPTURER, a dexterous aerial manipulator for centimeter-level grasping across diverse environments. To achieve the challenging goal, we …
@misc{wang2026capturer,
title = {{CAPTURER: A Dexterous Aerial Manipulator for Dynamic and Precise Grasping across Diverse Environments}},
author = {Meng Wang and Jindou Jia and Sicheng Zhou and Zeshuai Chen and Kexin Guo and Xiang Yu and Lei Guo},
year = {2026},
eprint = {2610.39119},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39119},
}
Li Guan, Rachael Granberry, Sahar Aseeri, Sahar Rostami et al.
Abstract摘要
Collecting naturalistic human manipulation demonstrations with accurate contact force and hand pose is essential for robot dexterous manipulation policy and foundational world model learning - yet no existing wearable platform provides both calibrated fingertip force sensing and occlusion-resilient hand tracking in a form factor that preserves natural manipulation behavior. We present Tactus, a textile-integrated sensing glove that co-optimizes three design pillars: (1) a thin, conformal 3D-knit form factor (1.5 mm fingertip stackup) designed to minimize interference with natural …
@misc{guan2026tactus,
title = {{Tactus: Tactile \& Pose Sensing Glove for Natural Dexterity Data Collection}},
author = {Li Guan and Rachael Granberry and Sahar Aseeri and Sahar Rostami and Taylor Niehues and Sai Dhanooshi Marri and Mia Huang and Dingtian Zhang and others},
year = {2026},
eprint = {2610.39076},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39076},
}
Arth Shukla, Kehlani Fay, Huaze Liu, Darin Djapri et al.
Abstract摘要
Catching arbitrary objects requires generalization to novel projectiles and real-time adaptation to unpredictable contact dynamics under noisy visual observations. This paper presents Catch It All!, a sim-to-real catching framework for humanoid robots that generalizes and adapts to unseen objects with variable physical properties for underhand and overhand catches, using only one egocentric camera. We learn policies which generalize to unseen objects with varying geometry, deformability, elasticity, and physical properties. Effective grasps which adapt real-time to object poses and shapes are …
@misc{shukla2026catch,
title = {{Catch It All!: Generalizable Dynamic Catching with a Dexterous Hand}},
author = {Arth Shukla and Kehlani Fay and Huaze Liu and Darin Djapri and Alexiy Buynitsky and Nicklas Hansen},
year = {2026},
eprint = {2610.39044},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39044},
}
Anas Houssaini, Hugo He, Junming Shi, Amin Abyaneh et al.
Abstract摘要
Human-level dexterity remains a central open problem in robotics, yet few simulation frameworks cover the full workflow of dexterous manipulation, from setting up a robot to collecting dexterous demonstrations and evaluating policies. We present DexSuite, an all-in-one simulation framework for dexterous manipulation built on the Genesis simulator. DexSuite provides 30 tasks and assembles its 10 arms and 12 end-effectors, from parallel-jaw grippers to dexterous hands, into more than 100 single-arm robots and thousands of bimanual pairs. It also ships a standardized environment API, integrated …
@misc{houssaini2026dexsuite,
title = {{DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks}},
author = {Anas Houssaini and Hugo He and Junming Shi and Amin Abyaneh and Ricardo Chahine and Shuo Wen and Mohamad H. Danesh and Charles-\'Etienne Fr\'echette and others},
year = {2026},
eprint = {2610.37799},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37799},
}
Ian Chuang, Hiya Shah, Vamsi Chilamkur, Iman Soltani et al.
Abstract摘要
Dexterous manipulation with multi-fingered hands is the next frontier of robotics, yet learned hand skills remain limited to simple grasps or basic repeated motions, such as rotating an object. As robots attempt more demanding in-hand maneuvers, safety, such as not dropping the object, becomes critical, yet it is rarely measured. We study in-hand object pose reaching, a task that demands diverse and difficult grasps: moving an object to almost any position and orientation within reach of the fingers, at any wrist orientation, through long sequences of consecutive goals. We present a recipe …
@misc{chuang2026pushing,
title = {{Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well}},
author = {Ian Chuang and Hiya Shah and Vamsi Chilamkur and Iman Soltani and Claire Tomlin},
year = {2026},
eprint = {2610.37652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37652},
}