@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},
}
@misc{wang2026never,
title = {{Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection}},
author = {Yi Wang and Wendi Chen and Zimo Wen and Han Xue and Xueqi Li and Wenye Yu and Zhijie Chen and Hao Yang and Jun Lv and Chuan Wen and Cewu Lu},
year = {2026},
eprint = {2607.14236},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.14236},
}
CoRL 2026 Spotlight. Brings contact into image-conditioned policies by rendering it, targeting the gap left by approaches that need tactile hardware, separate tactile encoders or non-image backbones.CoRL 2026 Spotlight。通过渲染接触信息把触觉带入基于图像的视觉运动策略，针对现有方法需要专用触觉硬件、独立触觉编码器或非图像主干，难以与预训练 2D 视觉表征策略兼容的问题。
@misc{dogan2026visible,
title = {{Visible Touch: Rendering Contact for Visuomotor Policies}},
author = {Metin Alp Dogan and Edward Sun and Feng Xu and Daniel Wu and Allen Peng and Dennis Hong and Yuchen Cui},
year = {2026},
eprint = {2609.14156},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.14156},
}
A coding agent that reads wrist force/torque as well as images, writes probing and force-feedback programs, and revises them from force logs; no demos or training. Beats its vision-only variant on 5 real tasks.同时读取腕部力/力矩与图像的编程智能体：编写接触探测与力反馈控制程序，并在每次执行后依据力记录诊断失败、修改代码；无需示教或训练。在 5 个真实任务上优于仅视觉版本。
@misc{tan2026forca,
title = {{FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation}},
author = {Xiao Tan and Yixuan Wang and Zhexi Wang and Wenzhen Yuan},
year = {2026},
eprint = {2610.31841},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31841},
}
36 contact-rich tasks built around eight everyday skills, with 3D-printable objects that each have a matching sim model. Reports that tested visuo-tactile policies did not clearly beat vision-only ones.围绕八种日常接触技能设计的 36 个接触密集型任务，40 多个物体可用消费级 3D 打印机复现，且每个都有对应仿真模型。报告称所测的视触觉策略并未明显优于纯视觉策略。
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},
}
A teacher sees future force/torque and optical flow; its features are distilled into a student using only current observations, so the policy anticipates contact. Tested on five real contact-rich tasks.教师网络看到未来的力/力矩与光流，再把动力学特征蒸馏给只用当前观测的学生网络，使策略在部署时能预判接触。在五个真实接触丰富任务上测试。
@misc{tang2026forceldm,
title = {{ForceLDM: A Force-aware Latent Dynamics Model for Contact-Rich Manipulation}},
author = {Peiyuan Tang and Yao Li and Xiaodong Zhang and Wuyang Zhang and Qin Xia and Yanyong Zhang and Zijiang Yang},
year = {2026},
eprint = {2611.03294},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03294},
}
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},
}
Open-source resistive tactile skin whose structured overlay turns shear into pressure patterns, so neighbouring taxels decode shear and normal force; ships with a design tool for custom skins plus hardware and software.开源电阻式触觉皮肤：结构化覆层把切向力转成空间压力差异，相邻触点即可解码剪切与法向力；附带可定制皮肤的设计工具，硬件和软件均开源。
@misc{slepyan2026opentac,
title = {{OpenTac: An Open Ecosystem for Customizable, Scalable, and Transferable 3D Tactile Sensing}},
author = {Ariel Slepyan and Geeta Chandra Raju Bethala and Jiafeng Gu and Anagha Rajendra Dangle and Yinzhe Zhou and Hongyuan Fu and Jade Sim and Tuhin Basu and Xinyi Yin and Md Faizal Karim and Gregory Hager and Homanga Bharadhwaj and Anand Bhattad and Krishna Murthy Jatavallabhula and Nitish V. Thakor},
year = {2026},
eprint = {2610.31661},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31661},
}
Fingertip sensor whose one camera switches illumination to alternate between see-through-membrane vision and tactile images — one shared viewpoint, no extrinsic calibration; fused by a transformer (VisTacFusion).指尖传感器只用一个摄像头，通过切换照明交替获取“透过膜的视觉图像”和触觉图像，视觉与触觉共享同一视角、无需相机外参标定；再用 Transformer（VisTacFusion）融合两者。
@misc{wang2026gelslim,
title = {{GelSlim 5.0: Unifying Vision and Touch with a Single Fingertip Camera}},
author = {Xiangdong Wang and I-Hsuan Huang and Xiaohao Xu and Xili Yi and Nima Fazeli and Xiaonan Huang},
year = {2026},
eprint = {2610.31334},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31334},
}
Review (Annual Review of Control, Robotics, and Autonomous Systems) tracing tactile manipulation from contact-mechanics models to learned policies: sensing basics, state estimation, IL, RL, VLAs, predictive models.综述（Annual Review of Control, Robotics, and Autonomous Systems 录用稿）：从接触力学建模到数据驱动学习，梳理触觉操作的发展，涵盖传感基础、状态表征、模仿学习、强化学习、VLA 与预测模型。
6 mm flexible array: silicone nubs over a piezoresistive layer turn shear into readable pressure patterns, giving 3-axis force per taxel; under 35 USD for 4x4. Reports it helps a Diffusion Policy insert a plug.6 毫米厚的柔性触觉阵列：压阻层上的硅胶凸点把切向力转成可区分的压力分布，实现每个触点的三轴力测量；4x4 阵列成本不到 35 美元。报告称其提升了扩散策略插插头任务的表现。
@misc{hou2026vectile,
title = {{VecTile: A Low-Cost Flexible Tactile Array for 3-DoF Force Sensing}},
author = {Jian Hou and Xin Zhou and Adam J. Spiers},
year = {2026},
eprint = {2610.31472},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31472},
}
Visuo-tactile world model trained only on humans handling GelSight sensors (4.9 h React dataset). Reports that contact dynamics transfer to robot rollouts more reliably than visual appearance does.只用人手持 GelSight 传感器的交互数据（4.9 小时 React 数据集）训练视触觉世界模型。报告在机器人回放中接触动态比视觉外观更容易迁移，部分任务的相机视角预测甚至不如直接复制帧。
@misc{ma2026acts,
title = {{ACTS: Learning Visuo-Tactile World Models from Human Physical Interaction}},
author = {Yuxiang Ma and Weihan Xu and Pengfei Ye and Anna Mei and Yuxin Ray Song and Tingle Li and Haonan Chen and Edward H. Adelson},
year = {2026},
eprint = {2610.24285},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24285},
}
CoRL 2026 Oral. NEXT estimates external joint torques without force sensors, training in 1 minute from 10 minutes of free-motion data, enabling force-feedback teleoperation and better policy learning on low-cost arms.CoRL 2026 Oral。NEXT 无需力传感器即可估计外部关节力矩，用 10 分钟自由运动数据训练 1 分钟，为低成本机械臂带来力反馈遥操作与更好的策略学习。
@misc{oh2026factr,
title = {{FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning}},
author = {Steven Oh and Jason Jingzhou Liu and Tony Tao and Philip Han and Kenneth Shaw and Satoshi Funabashi and Ruslan Salakhutdinov and Deepak Pathak},
year = {2026},
eprint = {2606.12406},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.12406},
}
CoRL 2026 Oral. Splits one tactile stream into three temporal roles, persistent states, abrupt contact events and future consequences, instead of a single fusion path in chunk-based insertion policies.CoRL 2026 Oral。不再用单一融合路径，而是把同一触觉流拆成三种时序角色：持续接触状态、突发接触事件与未来接触后果，用于分块式插接策略。
@misc{wang2026tactilestep,
title = {{TactileStep: Sole Tactile Learning for Regulating Foot-Terrain Interaction in Humanoid Locomotion}},
author = {Zizhuo Wang and Ming-ju Lee and Shaoting Zhu and Haozhe Lou and Hang Zhao and Yiming Li},
year = {2026},
eprint = {2609.28959},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.28959},
}
@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},
}
CoRL 2026 Spotlight. Dual-branch generative framework that extracts clean shading and marker images at once from one marker-based tactile frame, avoiding serial mask-based inpainting.CoRL 2026 Spotlight。双分支生成框架，可从单帧带标记点的触觉图像同时分离出干净的明暗图与标记图，避免串行掩码修复。
@misc{zhang2026detac,
title = {{DeTac: Dual-branch Decoupling of Texture and Force Signals in Vision-Based Tactile Sensors}},
author = {Zhirui Zhang and Yunkai Tang and Jiahang Li and Fan Fei and Jinnuo Zhang and Kairan Ding and Jieji Ren and Yandong Guo and Boxin Shi},
year = {2026},
eprint = {2610.16463},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.16463},
}
A compliant fingertip under $50 gives a dense 12×32 normal and shear map at 60 Hz. Visuo-tactile policies beat vision-only baselines by 46.7 points on average across scooping, pouring and test-tube tasks.每个成本低于 50 美元的柔顺指尖，以 60 Hz 输出 12×32 的法向与剪切稠密响应。在舀取、倾倒和试管任务上，视触觉策略平均比纯视觉基线高 46.7 个百分点。
@misc{binglin2026panotac,
title = {{PanoTac: A Dense Three-Axis Tactile Sensor for Robust Touch-Centric Manipulation}},
author = {Binglin and Wang and Xiaole and Jiang and Zicheng Teng and Xinqi Liu and Yunzhu Li and Lerrel Pinto and Zichen Jeff Cui and Binghao Huang},
year = {2026},
eprint = {2611.03520},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03520},
}
128 simulated tactile tasks in three regimes (Precise, Recognition, Dynamic) with 12 perturbations, plus a tactile world model that treats touch as an evolving state and refines actions with predicted contact.128 个仿真触觉任务，分精确、识别、动态三类，含 12 种扰动；并提出动作条件的触觉世界模型，把触觉当作随时间演化的状态，用预测的触觉信息修正动作。
@misc{shi2026tacmind,
title = {{TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation}},
author = {Jiaxin Shi and Zibin Wu and Yichi Zhang and Jianghua Duan and Siyu Zhu and Weihao Yuan},
year = {2026},
eprint = {2611.00455},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00455},
}
Plugs several pretrained tactile foundation models into Diffusion Policy and compares them in one setup, including fine-tuning and mid-training strategies, with over 5,000 real-world rollouts.把多种预训练触觉基础模型接入 Diffusion Policy，在统一设置下比较，并考察编码器微调与任务专用中训练策略，含 5000 多次真实机器人回合。
@misc{murooka2026evaluating,
title = {{Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation}},
author = {Masaki Murooka and Ryoichi Nakajo and Tomohiro Motoda and Koshi Makihara and Hanbit Oh and Keisuke Shirai and Yukiyasu Domae},
year = {2026},
eprint = {2611.00957},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00957},
}
Keeps a vision-based flow-matching policy and learns to steer its velocity field from tactile forecasts and live feedback, so touch can correct actions. Reports gains over baselines in six sim and six real tasks.保留视觉流匹配策略，用触觉预测与实时反馈学习对其速度场的残差引导，让触觉能修正动作。报告在六个仿真和六个真实任务上优于基线。
@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},
}
Adds a full-body tactile reflex that reaches the motors within 10 ms beneath a learned whole-body policy; on a G1 it cuts first force peak 34.1% versus the same reflex delayed by 20 ms.在学习型全身策略之下加入全身触觉反射，10 毫秒内直达电机；在 G1 上，与延迟 20 毫秒的相同反射相比，首次力峰值降低 34.1%。
@misc{wang2026reflexarc,
title = {{ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control}},
author = {Mingyu Wang and Si Li and Ziqing Chen and He Wang and Li Yi},
year = {2026},
eprint = {2611.04424},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04424},
}
VISTA: equivariant visuotactile diffusion policy that fuses touch into spherical visual tokens and rotates them with the gripper pose, for more data-efficient contact-rich imitation.VISTA：等变视触觉扩散策略，把触觉融合进球面视觉 token 并随末端姿态旋转，提升富接触模仿学习的数据效率。
@misc{wong2026equivariant,
title = {{Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation}},
author = {Lik Hang Kenny Wong and Yiyao Ma and Xiu-Shen Wei and Zelong Tan and Zhuheng Song and Dongsheng Xie and Kai Chen and Qi Dou},
year = {2026},
eprint = {2610.03333},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.03333},
}
TacEx points curiosity at touch: it splits model uncertainty by sense so exploration seeks contact, not free-space motion. The collected data trains pick-and-place offline and post-trains VLAs.TacEx 让好奇心聚焦触觉：按感知模态分解模型不确定性，使探索去寻找接触而非空中乱动。采集的数据可离线训练抓放策略，也可用于 VLA 后训练。
@misc{iten2026tactile,
title = {{Tactile Curiosity Drives Robot Interaction}},
author = {Klemens Iten and Alexander Proshkin and Bhavya Sukhija and Stelian Coros and Andreas Krause and Pieter Abbeel and Carmelo Sferrazza},
year = {2026},
eprint = {2609.40134},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.40134},
}
Adds separate tactile, force and torque streams to a pretrained VLA through joint cross-modal attention, with staged training and an auxiliary task that predicts future physical signals. Real-robot tests.为预训练 VLA 增加独立的触觉、力和力矩信号流，通过跨模态联合注意力融合，配合分阶段训练和预测未来物理信号的辅助任务。真机实验验证。
@misc{lee2026modular,
title = {{Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models}},
author = {Jimin Lee and Huiwon Jang and Myungkyu Koo and Jungwoo Park and Jinwoo Shin},
year = {2026},
eprint = {2604.23272},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.23272},
}
Simulation platform for visuo-tactile data supporting three common sensors, plus an encoder trained on its synthetic data and an 8-task benchmark. Reports +17.1% on the benchmark and +25% in real tasks.视触觉仿真数据合成平台，支持三种常用传感器，并基于合成数据训练视触觉编码器、发布 8 个任务的基准。报告基准成功率提升 17.1%，真机提升 25%。
@misc{chen2026univtac,
title = {{UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking}},
author = {Baijun Chen and Weijie Wan and Tianxing Chen and Xianda Guo and Congsheng Xu and Yuanyang Qi and Haojie Zhang and Longyan Wu and Tianling Xu and Zixuan Li and Yizhe Wu and Rui Li and Xiaokang Yang and Ping Luo and Wei Sui and Yao Mu},
year = {2026},
eprint = {2602.10093},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.10093},
}
Real-world RL that adds a lightweight tactile residual on top of any pretrained visual policy, bootstrapped from the base policy's own rollouts. Reports 5–40% success raised to 85–100% within 40–80 minutes.真实世界强化学习：在任意预训练视觉策略之上学习轻量的触觉残差修正，先用基础策略自主执行的数据冷启动。报告四个任务成功率在 40–80 分钟内从 5–40% 提升到 85–100%。
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},
}
Gives a VLA memory through compact force-history tokens from a pretrained VAE, for tasks like pressing a button a set number of times. Reports over 80% success on three memory-dependent tasks.用预训练 VAE 把力信号历史压缩成记忆 token 输入 VLA，解决按指定次数按按钮、擦盘子等依赖记忆的接触任务。报告三个记忆型任务成功率超过 80%。
@misc{li2026fm,
title = {{FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation}},
author = {Ruicheng Li and Qixiu Li and Ruichun Ma and Yu Deng and Lin Luo and Zhiying Du and Jianfeng Xiang and Huizhi Liang and Ruicheng Wang and Jiaolong Yang and Baining Guo},
year = {2026},
eprint = {2607.18231},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.18231},
}
Fuses an RGB camera, a GelSight Mini and a high-rate event-based tactile sensor for a flow-matching policy. Reports 80% average success on five contact-rich tasks vs 35.5% vision-only.融合 RGB 相机、GelSight Mini 与高频事件型触觉传感器的多分辨率表示，驱动流匹配策略。报告五个富接触任务平均成功率 80%，纯视觉仅 35.5%。
@misc{krohn2026multi,
title = {{Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation}},
author = {Rickmer Krohn and Erik Helmut and Niklas Funk and Jan Peters and Vignesh Prasad and Georgia Chalvatzaki},
year = {2026},
eprint = {2606.06281},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.06281},
}
Adds touch to a VLA via sim-real co-training and simulated RL in a real-aligned simulator, learning how contact should change actions near failure. Reports 72.5% vs 50.0% on four bimanual tasks.通过仿真-真机协同训练和在对齐真实的模拟器中做强化学习，为 VLA 加入触觉，学习接近失败时触觉应如何调整动作。报告四个双臂任务成功率 72.5%，基线 50.0%。
@misc{ma2026taccorl,
title = {{TacCoRL: Integrating Tactile Feedback into VLA via Simulation}},
author = {Siyu Ma and Yuqi Liang and Chang Yu and Yunuo Chen and Hao Su and Yixin Zhu and Yin Yang and Chenfanfu Jiang},
year = {2026},
eprint = {2606.11743},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.11743},
}
Tactile policy pretrained on ~3,000 hours from 26 sources and 21 sensors, mapping image, array and state signals into shared tokens. Reports +17.2% on seen setups and +31% on two unseen sensor setups.触觉基础策略：在 26 个数据源、21 种传感器约 3000 小时数据上预训练，把图像型、阵列型与状态型触觉信号映射到统一 token。报告已见配置提升 17.2%，两种未见传感器提升 31%。
@misc{yuan2026ftp,
title = {{FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation}},
author = {Chengbo Yuan and Zicheng Zhang and Mingjie Zhou and Wendi Chen and Yi Wang and Zhuoyang Liu and Dantong Niu and Shuo Wang and Hui Zhang and Wenkang Zhang and Yingdong Hu and Yuanqing Gong and Wanli Xing and Chuan Wen and Cewu Lu and Kaifeng Zhang and Yang Gao},
year = {2026},
eprint = {2606.13102},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.13102},
}
Wearable whole-body tactile interface for both human demos and humanoid teleop, with a planner that predicts poses plus contact forces for a tactile admittance controller. Five contact-rich tasks.可穿戴的全身触觉接口，可用于人类演示和人形遥操作；规划器同时预测末端位姿与接触力，作为触觉导纳控制器的参考。在五个富接触任务上验证。
@misc{jang2026wt,
title = {{WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning}},
author = {Jaehwi Jang and Zhaoyuan Gu and Alfred Cueva and Zimeng Chai and Junjie Sheng and Thong Nguyen and Himank Galundia and Yifan Wu and Huishu Xue and Isaac Legene and Ojas Mediratta and Davin Doan and Andrew Collins and Sarah Sadegh and KyoungMok Kim and Rishita Dhalbisoi and Zun Chen and Ye Zhao},
year = {2026},
eprint = {2606.13232},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.13232},
}
Shared tactile representation across sensors: sensor-specific encoders plus a common transformer, pretrained on a new 1.6M-frame paired dataset from four sensors; adapts to new tasks and unseen sensors.跨传感器的统一触觉表征：各传感器独立编码器加共享 Transformer 主干，在新的 160 万帧四传感器配对数据集上预训练，可迁移到新任务和未见传感器。
@misc{bi2026heterogeneous,
title = {{Heterogeneous Tactile Transformer}},
author = {Jianxin Bi and Qiang Wang and Jayaram Reddy and Kelvin Lin and Soibkhon Khajikhanov and Ruihan Gao and Harold Soh},
year = {2026},
eprint = {2606.29948},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.29948},
}
Inference-time steering with vision and touch: visual sampling picks the behaviour, tactile-guided diffusion editing fixes local contact, scored by a visuo-tactile latent world model. Reports +51% over the base policy.视觉+触觉的推理时策略引导：视觉采样验证决定执行何种行为，触觉引导的扩散编辑修正局部接触，并由视触觉潜在世界模型打分。报告比基础策略提升 51%。
@misc{wu2026inference,
title = {{Inference-time Policy Steering via Vision and Touch}},
author = {Yilin Wu and Zilin Si and Zeynep Temel and Oliver Kroemer and Andrea Bajcsy},
year = {2026},
eprint = {2606.14981},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.14981},
}
Adds force-torque sensing to a pretrained vision-only world-action model with staged fusion, multisensory future prediction and replay of pretraining data; strong on contact tasks without losing original skills.通过分阶段融合、多感官未来预测和预训练数据回放，把力/力矩传感加入预训练的纯视觉世界动作模型；在富接触任务上表现好，同时保住甚至提升原有任务性能。
@misc{clark2026multisensory,
title = {{Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force}},
author = {Jaden Clark and Changhao Wang and Yihuai Gao and Seongheon Hong and Hojung Choi and Mark Cutkosky and Yifan Hou and Shuran Song},
year = {2026},
eprint = {2606.30988},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.30988},
}
Low-cost wrist sEMG band whose model predicts per-finger forces, turning video demos into force-labelled ones after a short calibration. Reports >50% lower force error than vision and 87% on force-sensitive tasks.低成本腕戴式肌电（sEMG）设备，经短暂校准即可预测每根手指的力，把视频演示变成带力标注的演示。报告力预测误差比视觉方法低 50% 以上，力敏感任务成功率 87%。
@misc{he2026forceband,
title = {{ForceBand: Learning Forceful Manipulation with sEMG}},
author = {Botao He and Zhi Wang and Linna Kuang and Ishaan Ghosh and Jitendra Malik and Cornelia Fermuller and Tingfan Wu and Jiayuan Mao and Ruoshi Liu and Haozhi Qi and Yiannis Aloimonos},
year = {2026},
eprint = {2606.26093},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.26093},
}
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},
}
Uses a 25 Hz tactile reflex controller as a teacher during data collection so demos carry well-regulated grasp forces; tactile-free ACT then reaches 95% stable grasps on fragile cups. Disturbances still need the reflex.数据采集时用 25 Hz 触觉反射控制器当“老师”，让演示自带良好的抓握力；之后不用触觉的 ACT 策略在易碎杯子上达到 95% 稳定抓取。但抵抗外部扰动仍需保留反射控制器。
@misc{feng2026what,
title = {{What is the Better Curriculum: Controller-Shaped Grasping Behavior for Contact Force-Sensitive Manipulation}},
author = {Ziyan Feng and Zizhao Yuan and Yulong Fu and Yuxin He and Zhiyuan Zhang and Zhengjie Zhang and Jinni Zhou and Renjing Xu and Qiang Nie},
year = {2026},
eprint = {2609.25887},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.25887},
}
Learns object representations from tactile exploration, aligned with text descriptions of properties, and uses them as context for manipulation. Reports task success from 41% to 92% (seen) and 19% to 67% (unseen).通过触觉探索学习物体表征，并与物理属性的文本描述对比对齐，再作为语境指导操作策略。报告任务成功率在已见物体上从 41% 升到 92%，未见物体从 19% 升到 67%。
@misc{yang2026learning,
title = {{Learning with Object-centric Representations of Tactile Interactive Perception for Robot Manipulation}},
author = {Xinyi Yang and Zilin Si and Zhuowei Xu and Zeynep Temel and Oliver Kroemer},
year = {2026},
eprint = {2609.33235},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.33235},
}
Encoders for resistive, magnetic and vision-based tactile sensors are trained on paired contacts into one latent space, so a policy trained with one sensor transfers zero-shot to another on four manipulation tasks.为电阻式、磁式和视觉触觉传感器分别训练编码器，利用成对接触数据映射到同一潜空间；在四个操作任务中，用一种传感器训练的策略可零样本迁移到另一种。
@misc{park2026tactx,
title = {{TactX: Learning Shared Tactile Representations Across Diverse Sensors}},
author = {Junsung Park and Sachin Bhadang and Carmelo Sferrazza and Sha Yi and Xiaolong Wang},
year = {2026},
eprint = {2606.31236},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.31236},
}
Learns a structured latent space of behaviour styles (e.g. how much force to apply), and lets language reason over it instead of generating actions directly, for personalized tactile policies like wiping or folding.学习按行为风格（如施力大小）组织的结构化潜空间，让语言在其中推理用户偏好而非直接生成动作，从而实现个性化的触觉策略，如擦拭、叠衣。
@misc{robledo2026tacstyle,
title = {{TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations}},
author = {Kevin Robledo and Matías I. Torres Galaz and Kumar Dixhant Rai and Shelly Sara Ulman and Tasmia Tasrin and Heramb Nemlekar},
year = {2026},
eprint = {2606.14862},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.14862},
}
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},
}
Estimates mass, elasticity and friction visuotactilely during the grasp and applies the minimum safe grip force; adds a dataset of fruits and vegetables with measured physical properties.在抓取过程中用视触觉实时估计物体的质量、弹性和摩擦，从而施加刚好足够的最小夹持力；并提供一个测量了物理属性的水果蔬菜数据集。
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},
}
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},
}
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},
}
Learns visuo-tactile latents end-to-end inside a diffusion world-action model not frozen encoders; multi-task cross-modal losses avoid feature collapse. Reports up to 50% gains on real bolt screwing and nut mating.在扩散式世界-动作模型中端到端学习视触觉潜表示，而非使用冻结的预训练编码器；借助跨模态多任务学习避免特征坍缩。在真实拧螺栓、螺母配合等任务上报告最高 50% 的提升。
Action-conditioned world model that predicts multi-view RGB and tactile images together in one video space, post-trained for state consistency; used for closed-loop policy evaluation and GRPO updates.动作条件的视触觉世界模型：在统一的视频空间中同时预测多视角 RGB 与触觉图像，并经一致性后训练保证与真实任务状态吻合；可用于闭环策略评估和 GRPO 策略更新。
@misc{xing2026physical,
title = {{Physical Canvas: A Vision Tactile World Model For Robot Policy Evaluation And Learning}},
author = {Zebin Xing and Yuhang Zheng and Yupeng Zheng and Yichen Zhang and Zichen Li and Shangqing Zhou and Yujie Zang and Yuchen Liu and Zhongpu Xia and Haoran Li and Qichao Zhang},
year = {2026},
eprint = {2610.28613},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28613},
}
100 h of wearable-captured human loco-manipulation (mostly professional work) aligning egocentric video, whole-body and finger motion, touch and step-level text; reports 71.9% lower body error than baselines.用可穿戴设备采集的 100 小时人类移动操作数据，侧重职业场景，对齐第一视角视频、全身与手指动作、触觉和步骤级文字描述；其补全全身动作的方法报告身体关键点误差比最强基线低 71.9%。
@misc{zhou2026egoexplorer,
title = {{EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ}},
author = {Yunsong Zhou and Xuekun Jiang and Haiqi Shen and Hangxu Liu and Chaojian Shi and Yujie Liu and Jingyuan Li and Haoran Yang and Jiafei Cao and Dongzhan Zhou and Dong Wang and Yao Mu and Chunhua Shen and Weinan Zhang},
year = {2026},
eprint = {2610.18872},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.18872},
}
Instead of one universal gripper: a fixed robot-side coupling plus cheap tool-side adapters with tactile sensors turn household tools into swappable end effectors; imitation policies cook tofu and flip meat.不追求万能夹爪，而是用固定的机器人侧接口加低成本、带触觉传感器的工具侧适配器，把日常厨具变成可自动更换的末端执行器，兼容多种机器人平台；配套遥操作采集示范，模仿学习策略完成煎豆腐、拌沙拉、翻肉等工具任务。
@misc{zhang2026swapbot,
title = {{SwapBot: Tool-Centric Robot Manipulation via Interchangeable End-Effectors and Tactile Sensing}},
author = {Yunchu Zhang and Ray Song and Junhan Jiao and Liyiming Ke and Yuchen Yang and Ruihan Gao and Emma Romig and Siddhartha Srinivasa and Yiyue Luo},
year = {2026},
eprint = {2610.40646},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40646},
}
A slow latent diffusion policy plans action chunks while a fast policy reacts to tactile/force feedback; reports >35% higher scores than visual imitation baselines on three contact-rich tasks.慢速扩散策略在隐空间规划动作块，快速策略根据触觉/力反馈高频调整；论文报告在三个接触丰富任务上比纯视觉模仿学习基线总分高 35% 以上。
Low-cost bilateral teleop relays arm forces to the operator, and a curriculum corrupts vision less and less over training so policies learn to attend to force.低成本双边遥操作把机械臂受力反馈给操作者；训练时对视觉的破坏逐渐减弱，让策略学会关注力信息。
@misc{liu2025factr,
title = {{FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning}},
author = {Jason Jingzhou Liu and Yulong Li and Kenneth Shaw and Tony Tao and Ruslan Salakhutdinov and Deepak Pathak},
year = {2025},
eprint = {2502.17432},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.17432},
}
Learns one static-and-dynamic representation across different visuo-tactile sensors, and contributes TacQuad, an aligned multi-sensor tactile dataset.跨多种视触觉传感器学习统一的静态-动态表征，并贡献对齐的多传感器触觉数据集 TacQuad。
@misc{feng2025anytouch,
title = {{AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors}},
author = {Ruoxuan Feng and Jiangyu Hu and Wenke Xia and Tianci Gao and Ao Shen and Yuhao Sun and Bin Fang and Di Hu},
year = {2025},
eprint = {2502.12191},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2502.12191},
}
A high-density tactile sensor across a quadruped's back lets it carry unsecured cylinders over long distances, a task that usually needs mounting hardware.在四足机器人背部铺设高密度触觉传感器，无需固定装置即可长距离运输未固定的圆柱物体。
@misc{lin2025locotouch,
title = {{LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing}},
author = {Changyi Lin and Yuxin Ray Song and Boda Huo and Mingyang Yu and Yikai Wang and Shiqi Liu and Yuxiang Yang and Wenhao Yu and Tingnan Zhang and Jie Tan and Yiyue Luo and Ding Zhao},
year = {2025},
eprint = {2505.23175},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.23175},
}
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},
}
A broad outlook on tactile robotics, covering piezoresistive, piezoelectric, capacitive, magnetic and optical sensors and where the field is heading.对触觉机器人的综合展望，涵盖压阻、压电、电容、磁性和光学等传感方式及领域走向。
@misc{luo2025tactile,
title = {{Tactile Robotics: An Outlook}},
author = {Shan Luo and Nathan F. Lepora and Wenzhen Yuan and Kaspar Althoefer and Gordon Cheng and Ravinder Dahiya},
year = {2025},
eprint = {2508.11261},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.11261},
}
Generates realistic tactile images from one reference image plus contact force and pose, to scale tactile datasets across sensors. Augmented data improves insertion, imitation learning and weighing in real tests.以一张参考触觉图像加接触力与接触位姿为条件，生成逼真的触觉图像来扩充跨传感器的触觉数据。在插装、模仿学习与称重等真实任务中，增强数据均带来提升。
@misc{luo2025controltac,
title = {{ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation}},
author = {Dongyu Luo and Kelin Yu and Amir-Hossein Shahidzadeh and Cornelia Fermüller and Yiannis Aloimonos and Ruohan Gao},
year = {2025},
eprint = {2505.20498},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2505.20498},
}
DreamTacVLA feeds high-res tactile images with wrist and third-person views, aligns them spatially, then fine-tunes with a tactile world model that predicts future touch signals.DreamTacVLA 把高分辨率触觉图像与腕部、第三人称视角一起输入并做空间对齐，再用预测未来触觉信号的触觉世界模型微调。
@misc{ye2025learning,
title = {{Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation}},
author = {Guo Ye and Zexi Zhang and Xu Zhao and Shang Wu and Haoran Lu and Shihan Lu and Han Liu},
year = {2025},
eprint = {2512.23864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2512.23864},
}
GPU tactile simulation combining IPC and ABD; the authors report running 18 times faster than real time across thousands of parallel environments.结合 IPC 与 ABD 的 GPU 触觉仿真；作者报告在数千并行环境下达到实时速度的 18 倍。
@misc{li2025taccel,
title = {{Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation}},
author = {Yuyang Li and Wenxin Du and Chang Yu and Puhao Li and Zihang Zhao and Tengyu Liu and Chenfanfu Jiang and Yixin Zhu and Siyuan Huang},
year = {2025},
eprint = {2504.12908},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.12908},
}
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},
}
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},
}
@misc{higuera2024sparsh,
title = {{Sparsh: Self-supervised touch representations for vision-based tactile sensing}},
author = {Carolina Higuera and Akash Sharma and Chaithanya Krishna Bodduluri and Taosha Fan and Patrick Lancaster and Mrinal Kalakrishnan and Michael Kaess and Byron Boots and Mike Lambeta and Tingfan Wu and Mustafa Mukadam},
year = {2024},
eprint = {2410.24090},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24090},
}
UniTouch aligns tactile signals with vision, language and audio embeddings, enabling zero-shot touch understanding.UniTouch 将触觉信号与视觉、语言、音频嵌入对齐，实现零样本触觉理解。
@misc{yang2024binding,
title = {{Binding Touch to Everything: Learning Unified Multimodal Tactile Representations}},
author = {Fengyu Yang and Chao Feng and Ziyang Chen and Hyoungseob Park and Daniel Wang and Yiming Dou and Ziyao Zeng and Xien Chen and Rit Gangopadhyay and Andrew Owens and Alex Wong},
year = {2024},
eprint = {2401.18084},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2401.18084},
}
@misc{huang2024d,
title = {{3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing}},
author = {Binghao Huang and Yixuan Wang and Xinyi Yang and Yiyue Luo and Yunzhu Li},
year = {2024},
eprint = {2410.24091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24091},
}
@misc{bhirangi2024anyskin,
title = {{AnySkin: Plug-and-play Skin Sensing for Robotic Touch}},
author = {Raunaq Bhirangi and Venkatesh Pattabiraman and Enes Erciyes and Yifeng Cao and Tess Hellebrekers and Lerrel Pinto},
year = {2024},
eprint = {2409.08276},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.08276},
}
Digit 360: an open-design, finger-shaped sensor that senses force, fine texture, vibration and heat, with an on-board neural accelerator for reflex-like local processing.Digit 360：开放设计的指尖形触觉传感器，可感知力、细微纹理、振动和温度，并用板载神经网络加速器做类似反射弧的本地处理。
@misc{lambeta2024digitizing,
title = {{Digitizing Touch with an Artificial Multimodal Fingertip}},
author = {Mike Lambeta and Tingfan Wu and Ali Sengul and Victoria Rose Most and Nolan Black and Kevin Sawyer and Romeo Mercado and Haozhi Qi and Alexander Sohn and Byron Taylor and Norb Tydingco and Gregg Kammerer and Dave Stroud and Jake Khatha and Kurt Jenkins and Kyle Most and Neal Stein and Ricardo Chavira and Thomas Craven-Bartle and Eric Sanchez and Yitian Ding and Jitendra Malik and Roberto Calandra},
year = {2024},
eprint = {2411.02479},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.02479},
}
TVL pairs tactile and visual observations with language descriptions and trains a touch-vision-language encoder plus a model that describes how things feel.TVL 数据集将触觉与视觉观测配上语言描述，并训练触觉-视觉-语言编码器以及能描述物体触感的模型。
@misc{fu2024touch,
title = {{A Touch, Vision, and Language Dataset for Multimodal Alignment}},
author = {Letian Fu and Gaurav Datta and Huang Huang and William Chung-Ho Panitch and Jaimyn Drake and Joseph Ortiz and Mustafa Mukadam and Mike Lambeta and Roberto Calandra and Ken Goldberg},
year = {2024},
eprint = {2402.13232},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2402.13232},
}
GPU-based simulation of GelSight-style tactile images and contact forces in Isaac Gym, plus learning tools for sim-to-real transfer of visuotactile policies.在 Isaac Gym 中用 GPU 模拟 GelSight 式触觉图像与接触力，并提供用于视触觉策略仿真到真实迁移的学习工具。
@misc{akinola2024tacsl,
title = {{TacSL: A Library for Visuotactile Sensor Simulation and Learning}},
author = {Iretiayo Akinola and Jie Xu and Jan Carius and Dieter Fox and Yashraj Narang},
year = {2024},
eprint = {2408.06506},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2408.06506},
}
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},
}
A recurrent graph network estimates object state and latent physics from visuo-tactile history; the learned dynamics model drives MPC for tasks like dense packing.循环图网络从视触觉历史估计物体状态与潜在物理属性，学到的动力学模型用于 MPC，完成密集装箱等任务。
@misc{ai2024robopack,
title = {{RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing}},
author = {Bo Ai and Stephen Tian and Haochen Shi and Yixuan Wang and Cheston Tan and Yunzhu Li and Jiajun Wu},
year = {2024},
eprint = {2407.01418},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.01418},
}
Combines tactile perception with language so an embodied system can reason about physical properties learned by touch; contributes the PhysiCLeAR dataset.把触觉感知与语言结合，让具身系统对触摸获得的物理属性做常识推理；贡献 PhysiCLeAR 数据集。
@misc{yu2024octopi,
title = {{Octopi: Object Property Reasoning with Large Tactile-Language Models}},
author = {Samson Yu and Kelvin Lin and Anxing Xiao and Jiafei Duan and Harold Soh},
year = {2024},
eprint = {2405.02794},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.02794},
}
TaRF puts vision and touch in one 3D space: touch probes are registered to photos, and a conditional diffusion model estimates touch at unprobed positions.TaRF 把视觉与触觉放进同一三维空间：将触摸采样配准到照片，并用条件扩散模型估计未采样位置的触觉。
@misc{dou2024tactile,
title = {{Tactile-Augmented Radiance Fields}},
author = {Yiming Dou and Fengyu Yang and Yi Liu and Antonio Loquercio and Andrew Owens},
year = {2024},
eprint = {2405.04534},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2405.04534},
}
Differentiable tactile simulator with an FEM soft-body elastomer model, built for contact with many material types rather than mostly rigid objects.可微触觉仿真器，用 FEM 软体模型模拟弹性体，面向多种材料的接触，而非主要针对刚体。
@misc{si2024difftactile,
title = {{DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation}},
author = {Zilin Si and Gu Zhang and Qingwei Ben and Branden Romero and Zhou Xian and Chao Liu and Chuang Gan},
year = {2024},
eprint = {2403.08716},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.08716},
}
Brings GIPC soft-body contact and the Taxim and FOTS tactile simulators into Isaac Sim for GelSight Mini, with Isaac Lab RL environments.把 GIPC 软体接触与 Taxim、FOTS 触觉仿真器接入 Isaac Sim，模拟 GelSight Mini，并提供 Isaac Lab 强化学习环境。
@misc{nguyen2024tacex,
title = {{TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators}},
author = {Duc Huy Nguyen and Tim Schneider and Guillaume Duret and Alap Kshirsagar and Boris Belousov and Jan Peters},
year = {2024},
eprint = {2411.04776},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.04776},
}
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},
}
Successor to DTact - a compact open-source visuotactile sensor that reconstructs 3D contact shape and estimates 6D force/torque with a model that generalizes to unseen objects.DTact 的后续：紧凑的开源视觉触觉传感器，可重建三维接触形状，并用可泛化到新物体的模型估计六维力/力矩。
@misc{lin2023dtact,
title = {{9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation}},
author = {Changyi Lin and Han Zhang and Jikai Xu and Lei Wu and Huazhe Xu},
year = {2023},
eprint = {2308.14277},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2308.14277},
}
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},
}
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},
}
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},
}
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},
}
Learns tactile policies from demonstrations given with human hands, closing the gap of teleop where operators steer by sight while the policy needs touch.从人手直接演示学习触觉策略，弥补遥操作中“人靠视觉控制、策略却要用触觉”的落差。
@misc{yu2023mimictouch,
title = {{MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation}},
author = {Kelin Yu and Yunhai Han and Qixian Wang and Vaibhav Saxena and Danfei Xu and Ye Zhao},
year = {2023},
eprint = {2310.16917},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.16917},
}
Replaces the RGB camera of an optical tactile sensor with an event camera; touch processing runs online at 1000 Hz and senses vibrations up to 498 Hz.用事件相机取代光学触觉传感器的 RGB 相机；触觉处理以 1000 Hz 在线运行，可感知最高 498 Hz 的振动。
@misc{funk2023evetac,
title = {{Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation}},
author = {Niklas Funk and Erik Helmut and Georgia Chalvatzaki and Roberto Calandra and Jan Peters},
year = {2023},
eprint = {2312.01236},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.01236},
}
A dataset of paired video and GelSight touch collected by people probing objects in the wild, for cross-modal tactile learning.由人手持 GelSight 在真实场景中触碰物体采集的视觉-触觉配对数据集，用于跨模态触觉学习。
@misc{yang2022touch,
title = {{Touch and Go: Learning from Human-Collected Vision and Touch}},
author = {Fengyu Yang and Chenyang Ma and Jiacheng Zhang and Jing Zhu and Wenzhen Yuan and Andrew Owens},
year = {2022},
eprint = {2211.12498},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2211.12498},
}
Open-source tactile RL environments with sim-to-real transfer across three low-cost optical sensors (TacTip, DIGIT, DigiTac), for comparing them on the same tasks.开源触觉强化学习环境，支持三种低成本光学触觉传感器（TacTip、DIGIT、DigiTac）的仿真到真机迁移，便于在相同任务上比较。
@misc{lin2022tactile,
title = {{Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch}},
author = {Yijiong Lin and John Lloyd and Alex Church and Nathan F. Lepora},
year = {2022},
eprint = {2207.10763},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2207.10763},
}
A low-cost visuotactile sensor that reads contact depth from how dark each pixel gets as the gel is pressed, giving high-resolution 3D geometry without photometric stereo.低成本视觉触觉传感器：根据凝胶受压时每个像素变暗的程度直接读出接触深度，无需光度立体即可获得高分辨率三维形状。
@misc{lin2022dtact,
title = {{DTact: A Vision-Based Tactile Sensor that Measures High-Resolution 3D Geometry Directly from Darkness}},
author = {Changyi Lin and Ziqi Lin and Shaoxiong Wang and Huazhe Xu},
year = {2022},
eprint = {2209.13916},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2209.13916},
}
Fuses camera, contact microphone and GelSight touch with self-attention in an imitation-learned policy, studied on dense packing and pouring tasks.在模仿学习策略中用自注意力融合相机、接触麦克风和 GelSight 触觉，在密集装箱与倾倒任务上做了研究。
@misc{li2022see,
title = {{See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation}},
author = {Hao Li and Yizhi Zhang and Junzhe Zhu and Shaoxiong Wang and Michelle A Lee and Huazhe Xu and Edward Adelson and Li Fei-Fei and Ruohan Gao and Jiajun Wu},
year = {2022},
eprint = {2212.03858},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2212.03858},
}
A thin magnetic-elastomer skin read by magnetometers; cheap to make and swap, with learned calibration that carries over to new skins.由磁力计读取的薄磁性弹性体皮肤，制作和更换成本低，学习得到的标定可迁移到新皮肤上。
Simulates GelSight images from calibration examples (optical lookup + linear elastic deformation) instead of physics-based rendering, so tactile data can be generated quickly.用标定样本（光学查找表 + 线弹性形变）而不是基于物理的渲染来模拟 GelSight 图像，可快速生成触觉数据。
@misc{si2021taxim,
title = {{Taxim: An Example-based Simulation Model for GelSight Tactile Sensors}},
author = {Zilin Si and Wenzhen Yuan},
year = {2021},
eprint = {2109.04027},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.04027},
}
A slim GelSight-style finger that measures contact shape, force and slip at high resolution, designed to fit robot grippers.纤薄的 GelSight 类触觉手指，可高分辨率测量接触形状、力与滑移，便于装在机器人夹爪上。
@misc{taylor2021gelslim,
title = {{GelSlim 3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger}},
author = {Ian Taylor and Siyuan Dong and Alberto Rodriguez},
year = {2021},
eprint = {2103.12269},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2103.12269},
}
@misc{wang2020tacto,
title = {{TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors}},
author = {Shaoxiong Wang and Mike Lambeta and Po-Wei Chou and Roberto Calandra},
year = {2020},
eprint = {2012.08456},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2012.08456},
}
Learns a compact vision + force/torque representation with self-supervised objectives, then trains a peg-insertion policy with RL on top of it on a real robot.用自监督目标学习视觉 + 力/力矩的紧凑联合表征，再在其上用强化学习在真实机器人上训练插孔策略。
@misc{lee2018making,
title = {{Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks}},
author = {Michelle A. Lee and Yuke Zhu and Krishnan Srinivasan and Parth Shah and Silvio Savarese and Li Fei-Fei and Animesh Garg and Jeannette Bohg},
year = {2018},
eprint = {1810.10191},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1810.10191},
}
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},
}
Chaitanya Pallerla, Yihong Feng, Avisekh Tripathy, Yang Tian et al.
Abstract摘要
Woody breast (WB) myopathy is a spatially heterogeneous fibro-degenerative condition of the broiler Pectoralis major muscle. Existing mechanical reference methods rely on single-point or sequential compression measurements, limiting characterization of spatial hardness variability. This study presents TACTUS (Tactile Adaptive Conformal 3D Uniform Sensing), an automated tactile array that generates thickness-normalized, three-dimensional hardness maps at 20 anatomically distributed locations within approximately 30 s. TACTUS integrates 20 independently motorized tactuators with force-sensing …
@misc{pallerla2026tactus,
title = {{TACTUS: A Robotic Palpation System for Spatially Heterogeneous Hardness Mapping of Woody Breast Myopathy}},
author = {Chaitanya Pallerla and Yihong Feng and Avisekh Tripathy and Yang Tian and Pouya Sohrabipour and Amirreza Davar and Casey M. Owens and Cengiz Koparan and others},
year = {2026},
eprint = {2611.12695},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2611.12695},
}
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},
}
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},
}
Learned manipulation policies often rely on wrist-mounted cameras for close-range visual feedback that complements external views of the workspace. However, contact-rich manipulation requires establishing and maintaining contacts that can remain hidden even from wrist cameras. Tactile sensors mounted on fingers can reliably and directly measure contact, without needing visual access. In this work, we show that this feedback enables policies to recover the performance advantage of wrist cameras using scene and egocentric vision together with touch. Our approach incorporates two low-cost …
@misc{yang2026tactile,
title = {{Tactile Sensors Are an Effective Substitute for Wrist Cameras in Contact-Rich or Occluded Manipulation}},
author = {Aijia Yang and Arsh Tangri and Sergey Zakharov and Krishna Murthy Jatavallabhula},
year = {2026},
eprint = {2611.10490},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10490},
}
Wen Li, E. Magnus Adams, Zijie Xu, Jiawen Yao et al.
Abstract摘要
Wearable haptic interfaces deliver physical stimuli to the skin, yet perceived locations vary widely across users and diverge from actuator locations, particularly for emergent tactile-feedback techniques. We developed a wearable haptic input/output (I/O) device integrating a 36-channel electrotactile array with capacitive touch sensing, enabling stimulation and user-reported percept locations to be registered within the same body-referenced coordinate frame. In 24 participants, the device revealed electrotactile and referred-thermal perceptual maps with notable interparticipant variability. …
@misc{li2026wearable,
title = {{A wearable input/output interface learns personalized models of haptic perception}},
author = {Wen Li and E. Magnus Adams and Zijie Xu and Jiawen Yao and Aybala Esmer and Xiaoyue Ni and Bozhi Tian and Pedro Lopes},
year = {2026},
eprint = {2611.09708},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09708},
}
Recently, world-action models (WAMs) have emerged as a powerful framework for robot manipulation. By jointly modeling action generation and future observation prediction, WAMs can act and anticipate the outcomes of the actions. However, existing WAMs typically rely on frozen pretrained encoders trained with reconstruction, which may not produce representations well suited for robot control. In this paper, we tackle the problem of learning physicallygrounded, control-relevant representations for WAMs. We propose MDF++, a visuo-tactile world-action model that learns latent representations of …
Ao Li, Zhihao Yuan, Chubin Zhang, Kunrui Huang et al.
Abstract摘要
Egocentric video captures human-object interaction at scale but rarely includes tactile labels for hidden contact and pressure. We introduce TouchAnnotator, an ego-only bimanual tactile annotator trained with multi-source tactile supervision and privileged-view distillation. A canonical hand field unifies glove pressure, geometry-derived human contact, and robot fingertip deformation from six datasets and three embodiments, while source-aware masks preserve valid targets. During training, an EMA teacher observes synchronized ego and wrist views and distills hand-local representations and …
@misc{li2026touchannotator,
title = {{TouchAnnotator: Multi-Source Tactile Supervision with Privileged-View Distillation}},
author = {Ao Li and Zhihao Yuan and Chubin Zhang and Kunrui Huang and Sheng Xu and Zengjue Chen and Junjie Wang and Jiawei Li and others},
year = {2026},
eprint = {2611.09129},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.09129},
}
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},
}
In recent years, massively parallelized rigid-body simulation has played a central role in robotics research, enabling rapid progress in areas such as reinforcement learning, stochastic optimal control, and randomized smoothing-based trajectory optimization. Particularly in contact-rich settings, the scalability of such simulations in large part depends on well-optimized representations of collision geometry, often requiring a coarse approximation of the underlying surfaces using primitives such as spheres, capsules, cylinders, and boxes. Whenever the complexity of the surfaces or the …
@misc{beker2026lodos,
title = {{Lodos: A Lightweight Simulator Designed for Contact-Rich Interactions with Detailed Meshes}},
author = {Onur Beker and Andreas René Geist and Georg Martius},
year = {2026},
eprint = {2611.08872},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08872},
}
Haptic interactions, such as touching an object in virtual reality, involve detecting a plethora of sensations through the skin. Whilst tactile displays exist to simulate and manipulate all of these cues individually, little progress has been made constructing devices that can control multiple cues simultaneously. This paper presents a tactile display technology that offers both softness and temperature change. A prototype display, based on particle jamming and the thermal transfer of heated and cooled water, is evaluated in terms of its ability to change both stiffness and temperature. …
@misc{brown2026multimodal,
title = {{A Multimodal Haptic Interface for Simultaneous Softness and Thermal Modulation}},
author = {Joshua Brown and Fernando Bello and Lynette Jones},
year = {2026},
eprint = {2611.08398},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08398},
}
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},
}
Razan Khalifeh, Mohamad Yaacoub, Christian Gianoglio, Moustafa Saleh et al.
Abstract摘要
Tactile sensing provides information about object properties that can improve robotic grasping and manipulation. However, accurate object recognition during the early stages of grasping remains challenging, particularly under strict real-time constraints. This study presents a flexible, distributed piezoelectric tactile sensing system for early-stage object recognition. The system was experimentally evaluated using six everyday objects and three recognition approaches: feature-based classification using support vector machines (SVMs), raw-signal classification using 1D convolutional neural …
@misc{khalifeh2026flexible,
title = {{Flexible Piezoelectric Tactile Sensing System for Intelligent Object Recognition}},
author = {Razan Khalifeh and Mohamad Yaacoub and Christian Gianoglio and Moustafa Saleh and Maurizio Valle and Yahya Abbass},
year = {2026},
eprint = {2611.07779},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07779},
}
Guanren Qiao, Ruixiang Ouyang, Sheng Xu, Yueci Deng et al.
Abstract摘要
Contact-rich manipulation requires precise control under complex contact dynamics while remaining robust to diverse visual conditions. However, real-world reinforcement learning often overfits to the visual appearance of training scenes. We propose Semantics-to-Contact (S2C), a stagewise framework for visually robust contact-rich manipulation. The insight is to decompose the problem into two stages: semantic focusing and contact refinement. In the first stage, a vision-language model localizes task-relevant regions across visually diverse scenes, providing coarse spatial guidance and reducing …
@misc{qiao2026semantics,
title = {{Semantics-to-Contact: A Stagewise Framework for Robust Contact-Rich Manipulation}},
author = {Guanren Qiao and Ruixiang Ouyang and Sheng Xu and Yueci Deng and Yunxin Tai and Kui Jia and Guiliang Liu},
year = {2026},
eprint = {2611.07532},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07532},
}
Teleoperating a Remotely Operated Vehicle (ROV) in complex underwater environments presents unique challenges. Dynamic, three dimensional obstacles and unseen water currents pose real risks to expensive robotic hardware in environments in which recovery can be next to impossible. This paper explores the cognitive effects of haptic feedback to improve vehicle safety in such environments. An in-water study is analysed with a ROV exposed to controlled cross-currents, with environmental information presented via a soft and vibrating haptic touchpad. Results demonstrate that while encountering an …
@misc{brown2026cognitive,
title = {{Cognitive and Behavioural Effects of Haptic Feedback for ROV Teleoperation in Hazardous Environments}},
author = {Joshua Brown and Ildar Farkhatdinov and Michael Jenkin},
year = {2026},
eprint = {2611.06689},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.06689},
}
Xuhao Qin, Xiyan Huang, Haoyu Dong, Xi Zhang et al.
Abstract摘要
Vision-based tactile sensors capture multiple aspects of physical interaction within a single image stream, including contact appearance, geometry, indentation depth, and tangential deformation. However, existing visuo-tactile policies often directly process tactile observations as images, making it difficult to determine which physical cues contribute to policy learning. This paper presents a controlled real-robot study that compares physically interpretable tactile signals using the LeRobot platform, enhanced with our visuo-tactile perception system. From the same tactile stream, we derive …
Integrating six-axis tactile feedback into robotic interfaces requires compact sensors with accurate load measurement and rapid online reconstruction. This paper presents LumiTac, a low-profile, camera-free optoelectronic tactile sensor combining spatially distributed illumination with spectral encoding. Five photoemitters and five RGBW photoreceivers are mounted on opposing circuit boards coupled through translucent silicone. Load-induced interlayer motion modulates the internal light field under compression, shear, bending, and torsion. The sensing head measures 12 mm in diameter and 4.75 …
We study physics-based retargeting of human demonstrations for robot learning: finding robot controls that reproduce reference motions under contact dynamics. We propose GRASP (Gradient Recovery from Archived Samples for Proposals), which combines sampling-based exploration with gradient-guided refinement without requiring a differentiable simulator. GRASP-TO extends tempered sequential Monte Carlo by fitting local quadratic cost models to archived rollouts and using their gradients to guide Markov chain Monte Carlo rejuvenation. Metropolis-Hastings correction preserves the target …
@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},
}
Sherry Choi, Sitao Sunny Li, Joseph Romero, Lillian Chin et al.
Abstract摘要
Motor responses evoked by noxious stimulation are used to assess neurological function in hospitalized patients with disorders of consciousness. In standard bedside assessments, these stimuli are applied manually, resulting in substantial variation in force, duration, frequency, and location. Here, we present OUCH (Objective, Uniform, Controlled Haptic stimulation), a wrist-worn device that uses pneumatic actuation to deliver noxious mechanical stimuli. OUCH combines stacked pneumatic pouch actuators, a rigid indenter, and a compliant spacer designed to buckle at a set load, so that the …
@misc{choi2026ouch,
title = {{OUCH: Objective, Uniform, Controlled Haptic Stimulation for Automated Neurological Assessment}},
author = {Sherry Choi and Sitao Sunny Li and Joseph Romero and Lillian Chin and Ashwin G. Ramayya and Allison M. Okamura and Ava Chen},
year = {2026},
eprint = {2611.05741},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05741},
}
Multi-agent systems offer significant advantages in terms of flexibility and redundancy, enabling the efficient distribution of complex tasks among multiple robots. However, their deployment requires advanced control strategies to ensure optimal cooperation between autonomous agents and human operators. We propose a modification of the global connectivity maintenance algorithm to incorporate measurements taken in agent's local frame, using sensors with limited fields of view. This algorithm, responsible for the autonomous behavior of the multi-robot system, is combined with different adaptive …
Humanoid robots operating in cluttered environments must react to unexpected contacts before the first impact peak, which arrives within tens of milliseconds. Learned whole-body policies running at 50 Hz introduce a policy-cycle delay and may suppress a protective withdrawal as a tracking error. To address these challenges, we propose ARC (Ascending Reflex Control), a bottom-up framework that integrates instantaneous tactile reflexes with learned whole-body control. ARC consists of two components: an instantaneous reflex pathway and bottom-up reflex integration. The reflex pathway detects …
@misc{wang2026reflexarc,
title = {{ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control}},
author = {Mingyu Wang and Si Li and Ziqing Chen and He Wang and Li Yi},
year = {2026},
eprint = {2611.04424},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04424},
}
Contact-rich manipulation benefits from tactile feedback that captures both detailed contact geometry and interaction forces. When scooping grains, contact patterns help infer spoon orientation, while resistance transmitted through the grasp guides motion. To provide these complementary sensing capabilities, we present PanoTac, a low-cost, compliant tactile fingertip designed for dense three-axis tactile sensing. Each fingertip provides a $12 \times 32 \times 3$ tactile representation, with one normal ($z$) and two shear ($x$, $y$) response components at each taxel. The sensor operates at 60 …
@misc{binglin2026panotac,
title = {{PanoTac: A Dense Three-Axis Tactile Sensor for Robust Touch-Centric Manipulation}},
author = {Binglin and Wang and Xiaole and Jiang and Zicheng Teng and Xinqi Liu and Yunzhu Li and Lerrel Pinto and others},
year = {2026},
eprint = {2611.03520},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03520},
}
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},
}
Peiyuan Tang, Yao Li, Xiaodong Zhang, Wuyang Zhang et al.
Abstract摘要
Contact-rich manipulation requires fine-grained control and complex physical interactions with the environment, posing significant challenges for robot policy learning. Vision-only policies struggle with such tasks, as contact states and interaction forces are difficult to infer from visual observations alone. Existing methods mitigate this issue by incorporating force or tactile sensing. However, they often treat these signals as auxiliary observations without fully exploiting their role in modeling future interaction dynamics. To address this, we propose ForceLDM, a force-aware latent …
@misc{tang2026forceldm,
title = {{ForceLDM: A Force-aware Latent Dynamics Model for Contact-Rich Manipulation}},
author = {Peiyuan Tang and Yao Li and Xiaodong Zhang and Wuyang Zhang and Qin Xia and Yanyong Zhang and Zijiang Yang},
year = {2026},
eprint = {2611.03294},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03294},
}
KlaskTron is an open-hardware physical platform for two-player adversarial reinforcement learning, with a learned actuator model that supports zero-shot sim-to-real transfer of trained policies. Building on this platform, we contribute KlaskTron-IL: a multi-tier imitation learning dataset (80k human demonstration timesteps collected directly on the physical robot, 320k symmetry-augmented timesteps, and synthetic expert trajectories up to 5M steps generated by the zero-shot-validated PPO policy), and diagnostic baselines that expose three characteristic failure modes of standard IL methods …
Zhongkai Gu, Yang Liu, Wei Zhu, Mitsuhiro Hayashibe
Abstract摘要
Contact transitions allow humanoid robots to redistribute object support across the whole body, enabling manipulation beyond hand-only grasps. Human videos provide a scalable source of such transitions for robots to learn from. However, body occlusion disrupts object pose tracking and corrupts retargeted motion. Moreover, accurately reproducing the object trajectory does not ensure that the robot applies stabilizing forces at the intended contacts, risking slips or drops in later phases. To address these challenges, we propose ReContact, which reconstructs and retargets human-object …
@misc{gu2026recontact,
title = {{ReContact: Reconstruct and Retarget Human Contacts from Video for Contact-Rich Humanoid Manipulation}},
author = {Zhongkai Gu and Yang Liu and Wei Zhu and Mitsuhiro Hayashibe},
year = {2026},
eprint = {2611.02968},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02968},
}
Kaihong Ouyang, Zihao Su, Chengxuan Zheng, Weiyi Lai et al.
Abstract摘要
Long-horizon robotic assembly requires reliable perception and action across grasping, alignment, and insertion. Vision captures global object pose and scene context, where as fingertip touch provides local contact cues that are especially useful as contact states change. We present a visuotactile diffusion policy that combines four-step histories from two camera views and two fingertip tactile images to condition action-sequence generation. A recurrent temporal representation integrates recent observations from both modalities. To encourage it to capture how contact evolves under action, an …
@misc{ouyang2026visuo,
title = {{Visuo-tactile Diffusion Policy for Robotic Manipulation with Auxiliary Tactile Dynamics Prediction}},
author = {Kaihong Ouyang and Zihao Su and Chengxuan Zheng and Weiyi Lai and Dong Zhou},
year = {2026},
eprint = {2611.02677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02677},
}
Hengyi Zhou, Jiaqi Wang, Yilin Ma, Dezhong Tong et al.
Abstract摘要
Mechanical hysteresis and pneumatic loading complicate contact force estimation from deformation measurements in soft robots. This work presents a 3D-printed polyether block amide (PEBA) Kresling cell with magnetic angular sensing that supports three operating modes: force sensing at zero gauge pressure, force sensing with tunable stiffness under gauge positive pressure, and self-sensing actuation under negative gauge pressure. A multiscale gated recurrent unit (GRU) network estimates contact force from angular histories to account for the the combined effects of geometric deformation and …
@misc{zhou2026history,
title = {{History-Aware Force Sensing and Tunable Stiffness in a Pneumatic Kresling Origami Cell}},
author = {Hengyi Zhou and Jiaqi Wang and Yilin Ma and Dezhong Tong and Xiaonan Huang},
year = {2026},
eprint = {2611.02140},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02140},
}
Robotic surface exploration benefits from tactile interfaces that accommodate contact and capture variations in surface characteristics. Herein, we report MagPillar, a tactile sensor inspired by spider tactile hairs that combines a magnetic pillar array with three-axis Hall sensing. A full-array mechanical and magnetic-field model characterizes how pillar deformation affects the superposed fields measured across the array, providing a physical basis for interpreting its contact responses. The compliant pillar architecture translates contact with surface textures and geometric features into …
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},
}
Masaki Murooka, Ryoichi Nakajo, Tomohiro Motoda, Koshi Makihara et al.
Abstract摘要
Tactile sensing plays an important role in imitation learning for contact-rich manipulation by providing direct observations of physical contact with objects. Recently, several tactile foundation models (TFMs) have been proposed to learn general-purpose tactile representations through pretraining on large-scale tactile datasets. However, how these pretrained tactile representations should be leveraged for end-to-end imitation learning remains insufficiently explored. In this work, we integrate multiple TFMs into Diffusion Policy and systematically evaluate their effectiveness for contact-rich …
@misc{murooka2026evaluating,
title = {{Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation}},
author = {Masaki Murooka and Ryoichi Nakajo and Tomohiro Motoda and Koshi Makihara and Hanbit Oh and Keisuke Shirai and Yukiyasu Domae},
year = {2026},
eprint = {2611.00957},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00957},
}
Opening the door of unsecured furniture loads the frictional contacts at its supports; too large a pull slides or tips the object. A friction-cone constraint on the commanded wrench removes this failure mode but costs haptic transparency. We quantify the trade-off: an admissible wrench set built from contact friction cones and support-polygon tipping margins is shrunk for robustness to bounded mass and friction errors, and any command in the shrunken set provably leaves the furniture static (Prop. 1). We call this map the robust friction cone projection (RFCP). RFCP and its haptic guard admit …
Jiaxin Shi, Zibin Wu, Yichi Zhang, Jianghua Duan et al.
Abstract摘要
Tactile sensing provides robots with direct observations of contact physics, local geometry, and interaction dynamics, making it a vital source of feedback for contact-rich manipulation. However, existing tactile manipulation studies are often developed and evaluated within specific task settings, making it difficult to systematically compare how tactile information contributes under different interaction conditions. In this work, we develop a benchmark in simulation comprising 128 tasks organized around three complementary evaluation regimes: Precise, Recognition, and Dynamic. The benchmark …
@misc{shi2026tacmind,
title = {{TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation}},
author = {Jiaxin Shi and Zibin Wu and Yichi Zhang and Jianghua Duan and Siyu Zhu and Weihao Yuan},
year = {2026},
eprint = {2611.00455},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00455},
}
Yunchu Zhang, Ray Song, Junhan Jiao, Liyiming Ke et al.
Abstract摘要
Robot manipulation in everyday environments inherently involves substantial tool usage. Seemingly simple household tasks, such as cooking pan-fried tofu, require forceful cutting, delicate brushing, and precise placement using different tools under contact-rich interaction. Thus, the ability to autonomously swap and use task-appropriate tools is important for tool-centric manipulation. We introduce SwapBot, a modular, tool-centric end effector system featuring low-cost adapters that quickly convert common household tools into robot end effectors. Rather than pursuing a single universal …
@misc{zhang2026swapbot,
title = {{SwapBot: Tool-Centric Robot Manipulation via Interchangeable End-Effectors and Tactile Sensing}},
author = {Yunchu Zhang and Ray Song and Junhan Jiao and Liyiming Ke and Yuchen Yang and Ruihan Gao and Emma Romig and Siddhartha Srinivasa and others},
year = {2026},
eprint = {2610.40646},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40646},
}
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},
}
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},
}
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},
}
Haoyang Tian, Zhengwei Li, Houcheng Li, Xu Yang et al.
Abstract摘要
Experts can master fine hand-object manipulation skills, yet limited conscious perception of rapid tactile interactions hinders their ability to articulate the underlying control mechanisms. Badminton racket control requires rapid, fine hand-force coordination, providing a demanding testbed for uncovering these mechanisms. An 864-node tactile-inertial glove with synchronized 100-Hz acquisition recorded over 14,000 strokes from more than 70 participants across five skills. Inspired by sensorimotor control, our mechanics-grounded two-layer framework inferred object-driving and grip-stabilizing …
@misc{tian2026high,
title = {{High-resolution tactile observation makes hidden racket-control mechanisms learnable}},
author = {Haoyang Tian and Zhengwei Li and Houcheng Li and Xu Yang and Peigeng Gu and Xinyue Wang and Tairan Zhu and Shuo Jiang and others},
year = {2026},
eprint = {2610.35563},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35563},
}
Haibao Yu, Jiaqi Meng, Zijian Zhang, Bowen Jing et al.
Abstract摘要
We introduce PhysLadder, a hierarchical Sim-Real benchmark for evaluating physical interaction capabilities in contact-rich manipulation. Its 16 household tasks span three cumulative levels: contact selection and geometric alignment (L1), force regulation within sustained contact (L2), and coordinated contact reconfiguration and force regulation (L3). Each task is instantiated in physically and visually matched simulated and real-world environments, with shared task definitions and success criteria. We evaluate GPT-6 Astra zero-shot with three trials per task in each environment. Real-world …
@misc{yu2026how,
title = {{How High Can GPT-6 Astra Climb in Contact-Rich Manipulation? PhysLadder: A Hierarchical, Matched Sim-Real Benchmark for Physical Interaction}},
author = {Haibao Yu and Jiaqi Meng and Zijian Zhang and Bowen Jing and Lei Yang and Yue Hu and Chenchen Ge and Yiming Hou and others},
year = {2026},
eprint = {2610.34527},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34527},
}