@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},
}
Hardware, data and learning designed together: a low-cost wheeled humanoid with compliant grippers, teleoperation and robot-free human data collection, one whole-body controller and a shared policy pipeline.把硬件、数据和学习放在一起设计：低成本轮式人形、柔顺夹爪，遥操作与免机器人的人类数据采集，统一的全身控制器，以及人类与机器人示范共用的策略学习流程。
@misc{tao2026pixel,
title = {{Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation}},
author = {Tony Tao and Jason Jingzhou Liu and Ruslan Salakhutdinov and Deepak Pathak},
year = {2026},
eprint = {2611.03648},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03648},
}
@misc{chi2024universal,
title = {{Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots}},
author = {Cheng Chi and Zhenjia Xu and Chuer Pan and Eric Cousineau and Benjamin Burchfiel and Siyuan Feng and Russ Tedrake and Shuran Song},
year = {2024},
eprint = {2402.10329},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.10329},
}
PyTorch library with datasets, pretrained policies (ACT, Diffusion, SmolVLA) and drivers for low-cost arms.PyTorch 机器人学习库，提供数据集、预训练策略（ACT、Diffusion、SmolVLA）与低成本机械臂驱动。
@misc{zhao2023learning,
title = {{Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware}},
author = {Tony Z. Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
year = {2023},
eprint = {2304.13705},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2304.13705},
}
Open-source humanoid with 3D-printed modular gearboxes, buildable for under US$5,000; hardware designs, embedded code and training code are released.开源人形机器人，采用 3D 打印的模块化减速器，在美国可用不到 5000 美元搭建；硬件设计、嵌入式代码与训练框架均已开源。
@misc{chi2025demonstrating,
title = {{Demonstrating Berkeley Humanoid Lite: An Open-source, Accessible, and Customizable 3D-printed Humanoid Robot}},
author = {Yufeng Chi and Qiayuan Liao and Junfeng Long and Xiaoyu Huang and Sophia Shao and Borivoje Nikolic and Zhongyu Li and Koushil Sreenath},
year = {2025},
eprint = {2504.17249},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.17249},
}
Puts ALOHA on a mobile base; co-training with static ALOHA data boosts mobile tasks like cooking and opening cabinets.将 ALOHA 装上移动底盘；与静态 ALOHA 数据联合训练显著提升做饭、开柜等移动任务表现。
@misc{fu2024mobile,
title = {{Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation}},
author = {Zipeng Fu and Tony Z. Zhao and Chelsea Finn},
year = {2024},
eprint = {2401.02117},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.02117},
}
Unitree's official starting point for RL locomotion on Go2, H1 and G1 — train in Isaac Gym, check sim-to-sim in MuJoCo, then deploy to the real robot.宇树官方的强化学习运动控制入门仓库，支持 Go2、H1、G1：在 Isaac Gym 中训练，在 MuJoCo 中做 sim2sim 验证，再部署到真机。
@misc{cheng2024open,
title = {{Open-TeleVision: Teleoperation with Immersive Active Visual Feedback}},
author = {Xuxin Cheng and Jialong Li and Shiqi Yang and Ge Yang and Xiaolong Wang},
year = {2024},
eprint = {2407.01512},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.01512},
}
An open 3D-printed 6-DoF arm (leader + follower) costing roughly $100–200 per arm; the default LeRobot starter kit.开源 3D 打印 6 自由度机械臂（主从臂），每只约 100–200 美元，是 LeRobot 的默认入门套件。
Upgraded ALOHA bimanual rig (stronger grippers, gravity compensation, sturdier frame) with open hardware designs and a MuJoCo model for simulation.ALOHA 双臂平台的升级版（更强夹爪、重力补偿、更稳固的框架），开源硬件设计并提供 MuJoCo 仿真模型。
@misc{team2024aloha,
title = {{ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation}},
author = {ALOHA 2 Team and Jorge Aldaco and Travis Armstrong and Robert Baruch and Jeff Bingham and Sanky Chan and Kenneth Draper and Debidatta Dwibedi and Chelsea Finn and Pete Florence and Spencer Goodrich and Wayne Gramlich and Torr Hage and Alexander Herzog and Jonathan Hoech and Thinh Nguyen and Ian Storz and Baruch Tabanpour and Leila Takayama and Jonathan Tompson and Ayzaan Wahid and Ted Wahrburg and Sichun Xu and Sergey Yaroshenko and Kevin Zakka and Tony Z. Zhao},
year = {2024},
eprint = {2405.02292},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.02292},
}
The open-source Solo quadruped and its modular torque-controlled actuators, built from off-the-shelf and 3D-printed parts so labs can reproduce a dynamic legged robot.开源四足机器人 Solo 及其模块化力矩控制关节，采用现成件和 3D 打印件，便于各实验室复现动态腿式机器人。
@misc{grimminger2019open,
title = {{An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research}},
author = {Felix Grimminger and Avadesh Meduri and Majid Khadiv and Julian Viereck and Manuel Wüthrich and Maximilien Naveau and Vincent Berenz and Steve Heim and Felix Widmaier and Thomas Flayols and Jonathan Fiene and Alexander Badri-Spröwitz and Ludovic Righetti},
year = {2019},
eprint = {1910.00093},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1910.00093},
}
Sensorized human demos to whole-body policies: scene-aware retargeting, masking of mismatched views in training, then DAgger corrections. Reports 87.0% task progress on five tasks.把传感化的人类示范变成全身策略：结合重建的场景几何做重定向，训练时用重定向误差遮蔽不匹配的视角，再用 DAgger 人工纠正补足接触。作者报告五个任务平均进度 87.0%。
@misc{liu2026pixel,
title = {{Pixel-UMI: Learning Environment-Aware Whole-Body Manipulation from Sensorized Human Demonstrations}},
author = {Jason Jingzhou Liu and Tony Tao and Deepak Pathak and Ruslan Salakhutdinov},
year = {2026},
eprint = {2611.03647},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03647},
}
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},
}
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},
}
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},
}
A portable hand-held rig for collecting demonstrations with a high-DoF robot hand, using the same hand hardware for collection and deployment, so there is no retargeting from human hands and no full robot setup needed.便携手持式数据采集装置，面向高自由度机器人灵巧手；采集与部署使用同一只机械手，无需从人手动作重定向，也无需搭建完整机器人系统。
@misc{xie2026dexport,
title = {{DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection}},
author = {Amber Xie and Jiawei Fu and Xiaofeng Guo and Carmelo Sferrazza and Guanya Shi and Haozhi Qi},
year = {2026},
eprint = {2610.32969},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32969},
}
Fully linkage-driven humanoid hand with 3 actuated DoF per finger; a flexure hinge and spring-steel drive rods replace many tiny parts, so it prints on a budget 3D printer at near human-hand size.完全连杆驱动的人形灵巧手，每根手指 3 个主动自由度；用柔性铰链和弹簧钢驱动杆取代大量微型零件，可用入门级 3D 打印机制造，尺寸接近真人手。
@misc{adwani2026phd,
title = {{PHD-Hand: A Printable Humanoid Dexterous Hand with Linkage-Driven 3-DOA Digits}},
author = {Neel Adwani and Muhaiminul Islam Akash and Saffia Ouali and Ethan Liu and Cong Wang},
year = {2026},
eprint = {2610.29030},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29030},
}
One operator drives a Unitree Go2 + Piper arm with a leader arm and two handheld controllers, with sensorless force feedback; records a unified 11-D action reused at deployment (shown by fine-tuning MolmoAct2).单人即可同时操控 Unitree Go2 四足与 Piper 机械臂：主端机械臂加两个手柄，无需力传感器即可力反馈；ROS 2 管线记录与部署共用的 11 维统一动作，并通过微调 MolmoAct2 验证从采集到部署的全流程。
@misc{langella2026locopilot,
title = {{LocoPilot: Single-Operator Bilateral Teleoperation for Loco-Manipulation Policy Learning}},
author = {Antonio Langella and Giovanni Minelli and Giovanni B. Dessy and Lorenzo Amatucci and Giulio Turrisi and Victor Barasuol and Claudio Semini},
year = {2026},
eprint = {2610.27998},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27998},
}
Fully open UMI-style handheld gripper with a low-cost stereo camera for metric, low-drift trajectories, plus UMI-Loc-Bench-v1 for localization accuracy; hardware, software and data under a permissive license.完全开源的 UMI 式手持夹爪：3D 打印、标准 ISO 接口，用低成本双目相机获得公制尺度、低漂移的轨迹，并提出手持采集设备定位精度基准 UMI-Loc-Bench-v1；硬件、软件与数据以可商用许可证发布。
@misc{murali2026openjaws,
title = {{OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation}},
author = {Adithyavairavan Murali and Yash Oza and Karanbir Chahal and Neel Jawale and Silas De Vries and Hesam Rabeti and Erwin Coumans and Balakumar Sundaralingam and Stan Birchfield},
year = {2026},
eprint = {2610.36598},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36598},
}
Open-source wearable extra thumb: a tendon-driven finger with motors moved to the forearm; an imitation policy maps palm-camera images to joint positions, inferring intent without explicit triggers.开源的可穿戴“第六指”：紧凑的双腱驱动手指，电机移到前臂以减轻末端重量；模仿学习策略直接把掌心相机图像映射为关节位置，从视觉中推断使用者意图，无需显式手势触发，软硬件全部开源。
@misc{hou2026egothumb,
title = {{EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasp and Holding Assistance}},
author = {Jianyu Hou and Jiawei Fu and Weikang Wan and Minghui Jiang and Jiajun Li and Hao Su},
year = {2026},
eprint = {2610.37582},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37582},
}
Open-source bimanual UMI with camera, LiDAR-inertial sensing, compute and power on board: no external tracking or calibration; parallel SLAM outputs training-ready data. Releases 40 h of data and hardware files.全栈开源的双手 UMI 野外数据采集系统：手持设备集成相机、激光雷达-惯性传感、板载算力与电源，无需外部追踪或现场标定；异步 SLAM 流水线直接产出可训练数据集，并发布 40 小时数据、硬件设计与复现指南，用 π0.5 和 MolmoAct2 微调验证数据质量。
@misc{velamoor2026umi,
title = {{UMI-Live for In-The-Wild Bimanual Robotics Data Collection}},
author = {Govind Velamoor and Adrian Lin and Sam Jung and Spencer Mateega and Brandon Y. Yang and Amish Sethi},
year = {2026},
eprint = {2610.39385},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39385},
}
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 desktop testbed under $2,500 for adversarial multi-agent RL, built around the tabletop game KLASK: open CAD and BOM, an Isaac Lab digital twin, and a sim-to-real baseline with a learned actuator model.基于桌游 KLASK 的桌面级对抗多智能体强化学习平台，成本低于 2500 美元：开放 CAD 与物料清单，提供 Isaac Lab 数字孪生，以及采用学习型执行器模型的 sim-to-real 基线。
@misc{ramachandran2026klasktron,
title = {{KlaskTron: An Open-Source Platform for Physical Adversarial Multi-Agent RL}},
author = {Aswin Ramachandran and Jona Schulz and Maurus Derungs and Carlo Angelini and Tobias Meier and Raffaello D'Andrea},
year = {2026},
eprint = {2611.03138},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03138},
}
An open-source 56-DOF mobile bimanual robot with 7-DOF arms and 17-DOF hands for about US$8.1k in parts, with Apple Vision Pro teleoperation and a demonstrated imitation learning pipeline.开源 56 自由度移动双臂机器人，含 7 自由度手臂和 17 自由度灵巧手，零件成本约 8100 美元，支持 Apple Vision Pro 遥操作，并演示了模仿学习全流程。
@misc{cheng2026mabel,
title = {{MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning}},
author = {Qilong Cheng and Rooholla Khorrambakht and Mohamed Yassine Kabouri and Quang Nam Nguyen and Luca Macesanu and Haoran Zhou and Yipeng Wang and Ludovic Righetti},
year = {2026},
eprint = {2611.04083},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04083},
}
A humanoid arm whose force, speed and endurance targets come from human upper-limb measurements; reports a 20 kg peak payload (2.25x its own mass) and 20.6 m/s endpoint speed.人形手臂，其力量、速度和耐力指标取自人类上肢测量数据；报告峰值负载 20 公斤（自重的 2.25 倍），末端速度 20.6 米每秒。
@misc{kim2026raipal,
title = {{RAIPAL: A Powerful and Agile Humanoid Arm for Human Tasks}},
author = {Donggeon Kim and Hyungho Chris Choi and Donghyuk Choi and Jeongsoo Park and Choongin Lee and Chan Park and Jemin Hwangbo},
year = {2026},
eprint = {2611.04334},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04334},
}
Reuses a frozen multi-robot value function as a differentiable surrogate for robot design, avoiding a new co-design RL loop per robot; handles over 1100 design parameters.把冻结的多机器人价值函数作为可微代理模型来优化机器人设计，无需为每台机器人重跑协同设计 RL；可处理 1100 多个设计参数。
@misc{bohlinger2026shape,
title = {{Shape Your Body: Value Gradients for Multi-Embodiment Robot Design}},
author = {Nico Bohlinger and Jan Peters},
year = {2026},
eprint = {2606.00702},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.00702},
}
First autonomous vine (soft growing) robot: 19 cameras along its body feed an end-to-end visuomotor policy learned from demos, which steers through branches, up slopes and into confined spaces.首个自主藤蔓（软体生长）机器人系统：沿机身分布的 19 个摄像头为从演示学到的端到端视觉运动策略提供反馈，能穿过分叉结构、爬坡并进入狭窄空间。
@misc{qin2026panovine,
title = {{PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot}},
author = {Yimeng Qin and Xiaomeng Xu and William Heap and Aditi Oak and Shuran Song and Allison Okamura},
year = {2026},
eprint = {2606.22923},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.22923},
}
Diffusion transformer over a shared tokenization of robot bodies, states and actions that generates robot designs to track target motions. A fabricated optimised ALOHA design cut tracking error by over 70%.基于统一的机器人本体/状态/动作 token 表示的扩散 Transformer，按目标末端轨迹生成机器人设计。实际制作的优化版 ALOHA 跟踪误差降低 70% 以上。
@misc{ha2026transformer,
title = {{Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design}},
author = {Huy Ha and C. Karen Liu and Shuran Song},
year = {2026},
eprint = {2607.25798},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.25798},
}
Low-cost upper-limb exoskeleton that records whole-arm poses and joint torques with real-time haptic torque feedback; retargets to OpenArm, Franka and xArm for learning compliant whole-body policies.低成本上肢外骨骼：记录整臂构型与关节力矩，并提供实时力矩触觉反馈；可重定向遥操作 OpenArm、Franka 和 xArm，用于学习主动柔顺的全身策略。
@misc{liang2026universal,
title = {{Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback}},
author = {Litian Liang and Jingxi Xu and Xinda Qi and Yujun Cai and Houzhu Ding and Luqi Wang and Zhixin Sun and Jyh-Herng Chow and Ming Yang and Mark Cutkosky},
year = {2026},
eprint = {2606.14218},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.14218},
}
One wearable both teleoperates a wheeled bimanual robot and records robot-free demos; arms are mechanically aligned, so no IK or retargeting. With a 10-episode robot-grounded seed, 100 robot-free episodes suffice.同一套穿戴设备既能遥操作轮式双臂机器人，也能在机器人不在场时录制示教；手臂与机器人机械同构，无需逆运动学或重定向。只需 10 条有机器人参与的种子数据，其余 100 条可脱离机器人录制。
@misc{chen2026supersuit,
title = {{SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation}},
author = {Tongqing Chen and Hang Wu and Jiasen Wang and Xiaotao Li},
year = {2026},
eprint = {2603.06280},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2603.06280},
}
VR humanoid teleoperation for long tasks: a robot-mounted binocular fisheye, wide stereo display, head-decoupled free-looking and stabilization keep the operator aware of the surroundings.面向长时任务的人形机器人 VR 遥操作：机载双目鱼眼相机、宽视场立体显示、与机器人运动解耦的自由环视及画面稳定，让操作者持续掌握周围空间信息。
Human and robot wear the same hand exoskeleton, so encoders and wrist cameras match between collection and rollout; retargeting becomes paired supervision. 3.0x faster collection, 70.0% success on five tasks.人与机器人佩戴同一套手部外骨骼，采集与执行时关节编码器和腕部相机看到相同的机构，重定向变成成对监督。数据采集效率是外骨骼遥操作的 3.0 倍，五个任务平均成功率 70.0%。
@misc{yu2026seed,
title = {{SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration}},
author = {Tengbo Yu and Jiahao Wu and Daohan Li and Bingxu Chen and Hao Liu and Xiaojian Ma and Hangxin Liu},
year = {2026},
eprint = {2609.11753},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.11753},
}
Wearable dexterous UMI: a shared end-effector module (light hand, in-hand camera, fingertip touch) driven by an isomorphic glove, so collected and deployed observations and actions match. Eight real tasks.可穿戴的灵巧 UMI：共享的末端模块（轻量灵巧手、手内相机、指尖触觉）由同构手套驱动，采集与部署时的观测和动作完全一致，无需重定向。在八个真机任务上验证。
@misc{xu2026realdexumi,
title = {{RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning}},
author = {Chaoyi Xu and Yixuan Jiang and Jiahui Huan and Yuhui Fu and Haoyu Zhou and Weitian Yuan and Jiayi Yu and Wanpeng Zhang and Haoqi Yuan and Zongqing Lu},
year = {2026},
eprint = {2606.06033},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.06033},
}
Few-shot calibration of biased mocap gloves or hand trackers: a HyperNet reads a few paired poses and predicts LoRA updates for a frozen MANO estimator, with no per-user fine-tuning.对存在偏差的动捕手套或手部追踪做少样本校准：HyperNet 读取少量成对姿态，为冻结的 MANO 手部估计模块预测 LoRA 式更新，无需逐用户微调。
@misc{zhang2026yoco,
title = {{YOCO: You Only Calibrate Once! Fast Mocap Calibration for Dexterous Teleoperation}},
author = {Yu Zhang and Yunqi Li and Yushi Du and Yi Ma and Yanchao Yang},
year = {2026},
eprint = {2610.11657},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.11657},
}
A cheap drop-in finger module for parallel grippers: two soft belts keep normal open/close and add in-hand translation, pitch and roll; used with MPC, a light 10-DoF teleop interface and learned policies.低成本的平行夹爪升级手指模组：双软皮带结构保留常规开合，并增加手内平移、俯仰与翻滚三个自由度；配合 MPC、轻量的 10 自由度遥操作接口和学习策略使用。
@misc{zhang2026belt,
title = {{Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation}},
author = {Boya Zhang and Andreas Zell and Georg Martius},
year = {2026},
eprint = {2606.20193},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.20193},
}
Adds a cheap clutch and 3D-printed spring to each joint of a passive leader arm so redundant joints return to a neutral pose, making demos more consistent; block stacking success 73.3% vs 36.7%.为被动主臂的每个关节加装低成本离合器和 3D 打印螺旋弹簧，使冗余关节回到中立姿态，让示教数据更一致；堆积木成功率 73.3%，未加时为 36.7%。
Active perception learned from human demonstrations: a 6-DoF robotic neck and a VR interface let bimanual robots search, track and focus.从人类示教学习主动感知：6 自由度机械颈加 VR 界面，让双臂机器人能搜索、跟踪和聚焦目标。
@misc{xiong2025vision,
title = {{Vision in Action: Learning Active Perception from Human Demonstrations}},
author = {Haoyu Xiong and Xiaomeng Xu and Jimmy Wu and Yifan Hou and Jeannette Bohg and Shuran Song},
year = {2025},
eprint = {2506.15666},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.15666},
}
A small, 3D-printed open-source humanoid with a matching simulation model, built so that ML policies and teleoperated data transfer to the real robot.3D 打印的小型开源人形机器人，配套仿真模型，便于机器学习策略与遥操作数据迁移到真机。
@misc{shi2025toddlerbot,
title = {{ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation}},
author = {Haochen Shi and Weizhuo Wang and Shuran Song and C. Karen Liu},
year = {2025},
eprint = {2502.00893},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.00893},
}
A whole-body teleoperation interface plus a learning method for a wheeled bimanual robot doing long household tasks that need reach, balance and navigation.面向轮式双臂机器人的全身遥操作接口与学习方法，用于需要伸展、平衡与导航的长程家务任务。
@misc{jiang2025behavior,
title = {{BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities}},
author = {Yunfan Jiang and Ruohan Zhang and Josiah Wong and Chen Wang and Yanjie Ze and Hang Yin and Cem Gokmen and Shuran Song and Jiajun Wu and Li Fei-Fei},
year = {2025},
eprint = {2503.05652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.05652},
}
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},
}
Open-source whole-body humanoid teleoperation — an arm exoskeleton plus motion-sensing gloves, with a foot pedal steering an RL walking controller; the cockpit hardware costs about US$500.开源的人形机器人全身遥操作系统：手臂外骨骼加动作捕捉手套，脚踏板驱动强化学习行走控制器；整套座舱硬件约 500 美元。
@misc{ben2025homie,
title = {{HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit}},
author = {Qingwei Ben and Feiyu Jia and Jia Zeng and Junting Dong and Dahua Lin and Jiangmiao Pang},
year = {2025},
eprint = {2502.13013},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.13013},
}
Exoskeleton demos are converted into pseudo-robot demonstrations; the paper reports its RISE-2 policy trained on them alone, with no robot data, performs comparably to one trained on teleoperated robot data.将外骨骼示教转换为“伪机器人”示教；论文报告仅用这些数据（无任何机器人数据）训练的 RISE-2 策略，效果与用遥操作机器人数据训练的策略相当。
@misc{fang2025airexo,
title = {{AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons}},
author = {Hongjie Fang and Chenxi Wang and Yiming Wang and Jingjing Chen and Shangning Xia and Jun Lv and Zihao He and Xiyan Yi and Yunhan Guo and Xinyu Zhan and Lixin Yang and Weiming Wang and Cewu Lu and Hao-Shu Fang},
year = {2025},
eprint = {2503.03081},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.03081},
}
DexUMI — an exoskeleton matched to the robot hand's fingertip workspace, plus inpainting that swaps the human hand for the robot's; reports 86% average success (Inspire, XHand) and ~3.2× faster collection than teleop.DexUMI：按目标机器人手的指尖工作空间设计的可穿戴手部外骨骼，再用视频修补把画面中的人手换成机器人手；论文报告在 Inspire 与 XHand 上平均成功率 86%，采集效率约为遥操作的 3.2 倍。
@misc{xu2025dexumi,
title = {{DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation}},
author = {Mengda Xu and Han Zhang and Yifan Hou and Zhenjia Xu and Linxi Fan and Manuela Veloso and Shuran Song},
year = {2025},
eprint = {2505.21864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.21864},
}
An open-source teleoperation glove buildable in hours for under $600, with 21-DoF hand capture, 5-DoF cable-driven force feedback and fingertip haptics; reports the feedback matters for contact-rich tasks without vision.开源遥操作手套，几小时即可组装、成本低于 600 美元：21 自由度手部动捕、5 自由度绳驱力反馈和指尖振动触觉；报告在无视觉输入时力/触觉反馈对接触密集任务至关重要，采集数据可训练模仿学习策略。
@misc{zhang2025doglove,
title = {{DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove}},
author = {Han Zhang and Songbo Hu and Zhecheng Yuan and Huazhe Xu},
year = {2025},
eprint = {2502.07730},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.07730},
}
A human-sized, tendon-driven hand (5 fingers, 15 underactuated DoF) from 3D-printed and off-the-shelf parts; joint-to-actuator control is learned from MANUS-glove mocap data instead of hand-modelled.人手大小的腱驱动灵巧手（5 指、15 个欠驱动自由度），由 3D 打印件和现成零件制成；关节到电机的控制映射用 MANUS 数据手套的动捕数据学习得到，而非手工建模。
@misc{zorin2025ruka,
title = {{RUKA: Rethinking the Design of Humanoid Hands with Learning}},
author = {Anya Zorin and Irmak Guzey and Billy Yan and Aadhithya Iyer and Lisa Kondrich and Nikhil X. Bhattasali and Lerrel Pinto},
year = {2025},
eprint = {2504.13165},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.13165},
}
Open-source biped built from e-commerce parts by sheet-metal welding, avoiding the size and fragility limits of 3D-printed designs.用电商零件和钣金焊接搭建的开源双足机器人，避开 3D 打印方案在尺寸和强度上的限制。
@misc{kawaharazuka2025mevita,
title = {{MEVITA: Open-Source Bipedal Robot Assembled from E-Commerce Components via Sheet Metal Welding}},
author = {Kento Kawaharazuka and Shogo Sawaguchi and Ayumu Iwata and Keita Yoneda and Temma Suzuki and Kei Okada},
year = {2025},
eprint = {2508.17684},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.17684},
}
@misc{wang2024dexcap,
title = {{DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation}},
author = {Chen Wang and Haochen Shi and Weizhuo Wang and Ruohan Zhang and Li Fei-Fei and C. Karen Liu},
year = {2024},
eprint = {2403.07788},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07788},
}
@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},
}
@misc{liao2024berkeley,
title = {{Berkeley Humanoid: A Research Platform for Learning-based Control}},
author = {Qiayuan Liao and Bike Zhang and Xuanyu Huang and Xiaoyu Huang and Zhongyu Li and Koushil Sreenath},
year = {2024},
eprint = {2407.21781},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.21781},
}
Apple Vision Pro teleoperation of two dexterous robot hands with real-time retargeting, collision and singularity avoidance, and low-cost vibration haptics for the operator.用 Apple Vision Pro 实时遥操作双灵巧手，带实时重定向、碰撞与奇异点规避，并为操作者提供低成本振动触觉反馈。
@misc{ding2024bunny,
title = {{Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning}},
author = {Runyu Ding and Yuzhe Qin and Jiyue Zhu and Chengzhe Jia and Shiqi Yang and Ruihan Yang and Xiaojuan Qi and Xiaolong Wang},
year = {2024},
eprint = {2407.03162},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.03162},
}
One modular teleop interface that mixes RGB-D body tracking, VR controllers, keyboard and joystick for whole-body mobile manipulators (Tiago++, HSR, Fetch), in sim and real.模块化遥操作接口，可组合 RGB-D 人体追踪、VR 手柄、键盘与摇杆，控制 Tiago++、HSR、Fetch 等移动操作机器人做全身动作，仿真和真机都支持。
@misc{dass2024telemoma,
title = {{TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation}},
author = {Shivin Dass and Wensi Ai and Yuqian Jiang and Samik Singh and Jiaheng Hu and Ruohan Zhang and Peter Stone and Ben Abbatematteo and Roberto Martín-Martín},
year = {2024},
eprint = {2403.07869},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07869},
}
ACE — a 3D-printed bimanual exoskeleton for wrist pose plus hand-facing webcams for finger pose (about $600); one setup teleoperates arm-hand systems, humanoids and quadruped-mounted arms.ACE：3D 打印的双臂外骨骼获取手腕位姿、朝向手部的摄像头估计手指姿态（约 600 美元）；同一套设备可遥操作机械臂+灵巧手、人形机器人以及四足搭载的机械臂。
@misc{yang2024ace,
title = {{ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation}},
author = {Shiqi Yang and Minghuan Liu and Yuzhe Qin and Runyu Ding and Jialong Li and Xuxin Cheng and Ruihan Yang and Sha Yi and Xiaolong Wang},
year = {2024},
eprint = {2408.11805},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2408.11805},
}
HATO — bimanual setup from two UR5e arms and repurposed prosthetic hands with fingertip touch sensors, VR teleop for data collection, and visuotactile policies for tasks like pouring and handovers.HATO：用两台 UR5e 机械臂和改装的假肢手（指尖带触觉传感器）搭建双臂系统，用 VR 遥操作采集数据，并训练视觉-触觉策略完成倒酒、交接等任务。
@misc{lin2024learning,
title = {{Learning Visuotactile Skills with Two Multifingered Hands}},
author = {Toru Lin and Yu Zhang and Qiyang Li and Haozhi Qi and Brent Yi and Sergey Levine and Jitendra Malik},
year = {2024},
eprint = {2404.16823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2404.16823},
}
An inexpensive open-source mobile base with powered casters, so it is fully holonomic; carries arbitrary arms and comes with phone teleoperation for collecting mobile-manipulation demos for imitation learning.低成本开源移动底盘，采用主动脚轮实现全向（完整约束）运动，可搭载任意机械臂，并配有手机遥操作界面，方便为模仿学习采集移动操作示教数据。
@misc{wu2024tidybot,
title = {{TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning}},
author = {Jimmy Wu and William Chong and Robert Holmberg and Aaditya Prasad and Yihuai Gao and Oussama Khatib and Shuran Song and Szymon Rusinkiewicz and Jeannette Bohg},
year = {2024},
eprint = {2412.10447},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.10447},
}
Robot-free UMI gripper demos train the manipulation policy; a sim-trained whole-body controller tracks its end-effector trajectories on a quadruped. Reports >70% success on all tasks and zero-shot reuse of an arm policy.用手持 UMI 夹爪采集无需机器人的示教来训练操作策略，再由仿真训练的全身控制器在四足机器人上跟踪其输出的末端轨迹；报告所有任务成功率超过 70%，并能零样本复用原为固定机械臂训练的策略。
@misc{ha2024umi,
title = {{UMI-on-Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers}},
author = {Huy Ha and Yihuai Gao and Zipeng Fu and Jie Tan and Shuran Song},
year = {2024},
eprint = {2407.10353},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.10353},
}
@misc{shaw2024bimanual,
title = {{Bimanual Dexterity for Complex Tasks}},
author = {Kenneth Shaw and Yulong Li and Jiahui Yang and Mohan Kumar Srirama and Ray Liu and Haoyu Xiong and Russell Mendonca and Deepak Pathak},
year = {2024},
eprint = {2411.13677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.13677},
}
Open-source 10-DoF humanoid for locomotion research; an RL policy using passive dynamics cuts cost of transport by up to 50% in sim and 31% on hardware.面向运动研究的开源 10 自由度人形；利用被动动力学的强化学习策略将运输成本在仿真中最多降低 50%，实机降低 31%。
@misc{xia2024duke,
title = {{The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics}},
author = {Boxi Xia and Bokuan Li and Jacob Lee and Michael Scutari and Boyuan Chen},
year = {2024},
eprint = {2409.19795},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.19795},
}
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},
}
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},
}
@misc{wu2023gello,
title = {{GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators}},
author = {Philipp Wu and Yide Shentu and Zhongke Yi and Xingyu Lin and Pieter Abbeel},
year = {2023},
eprint = {2309.13037},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.13037},
}
Camera-only teleoperation for arm + dexterous hand pairs - tracks the operator's hand and retargets it to many different robot hands, in sim or on real hardware.仅用相机即可遥操作"机械臂 + 灵巧手"：跟踪操作者手部并重定向到多种不同的机器人手，可用于仿真或真机。
@misc{qin2023anyteleop,
title = {{AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System}},
author = {Yuzhe Qin and Wei Yang and Binghao Huang and Karl Van Wyk and Hao Su and Xiaolong Wang and Yu-Wei Chao and Dieter Fox},
year = {2023},
eprint = {2307.04577},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.04577},
}
A portable dual-arm exoskeleton (about $600 in parts, per the paper) for joint-level teleop and cheap in-the-wild demos; reports few-minute teleop plus wild data rivals much longer teleop-only training.便携式双臂外骨骼（论文称零件成本约 600 美元），可做关节级遥操作并低成本采集野外示教；报告称少量遥操作数据加野外数据即可达到甚至超过长时间纯遥操作训练的效果。
@misc{fang2023airexo,
title = {{AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild}},
author = {Hongjie Fang and Hao-Shu Fang and Yiming Wang and Jieji Ren and Jingjing Chen and Ruo Zhang and Weiming Wang and Cewu Lu},
year = {2023},
eprint = {2309.14975},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.14975},
}
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},
}
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。
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},
}
The design of Hello Robot's Stretch — a slim, lightweight mobile manipulator with a telescoping arm on a lift, built for homes and used widely in home-robot research.Hello Robot 的 Stretch 设计：在升降柱上装伸缩臂的轻巧移动操作机器人，面向家庭环境，被大量家庭机器人研究采用。
@misc{kemp2021design,
title = {{The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments}},
author = {Charles C. Kemp and Aaron Edsinger and Henry M. Clever and Blaine Matulevich},
year = {2021},
eprint = {2109.10892},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.10892},
}
Xianlong Mai, Rui Yu, Rui Chen, Daniele Leonardis et al.
Abstract摘要
Hand kinesthetic feedback is crucial for restoring and enhancing human perception of force and displacement in robotic hand teleoperation. Wide perception range of force feedback is vital in rendering objects of different stiffness, especially for high-stiffness rendering. However, existing approaches struggle to achieve wide range of force feedback while maintaining compact and wearable design. This paper presents a hand exoskeleton system employing magnetorheological (MR) brakes to provide a wide range kinesthetic feedback during teleoperation. The MR brake provides a braking torque ranging …
@misc{mai2026compact,
title = {{Compact and Wide-Force-Range Magnetorheological Hand Exoskeleton for Kinesthetic Feedback in Teleoperation}},
author = {Xianlong Mai and Rui Yu and Rui Chen and Daniele Leonardis and Wenbo Li and Domenico Chiaradia and Antonio Frisoli and Shuaishuai Sun},
year = {2026},
eprint = {2611.12130},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12130},
}
Roshan S. Kaundinya, Hehui Zheng, Robert K. Katzschmann, George Haller
Abstract摘要
Reinforcement learning (RL) requires large amounts of interaction data or a reliable simulator, both hard to obtain for nonlinear soft robotic hardware. Recent work shows that a time-scale separation inherent to soft robot dynamics justifies their reduction to low-dimensional invariant manifolds, known as adiabatic spectral submanifolds (aSSMs). We demonstrate that an aSSM-reduced model, a simple polynomial ordinary differential equation learned purely from pre-collected data, can serve as an RL training environment. On a pneumatically actuated soft arm, we train a trajectory-tracking policy …
@misc{kaundinya2026accelerated,
title = {{Accelerated Reinforcement Learning for Soft Robotics: Hours Instead of Years}},
author = {Roshan S. Kaundinya and Hehui Zheng and Robert K. Katzschmann and George Haller},
year = {2026},
eprint = {2611.11718},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11718},
}
Roshan S. Kaundinya, Mark Leone, Roberto Sabella, Hugo Buurmeijer et al.
Abstract摘要
A purely data-driven method grounded in the theory of adiabatic spectral submanifolds (aSSMs) was recently used to model and control nonlinear soft robotic simulators. Here, we extend the methodology to learn aSSM-reduced models from data collected from hardware soft robotic platforms. Specifically, we learn a five dimensional (5D) aSSM-reduced nonlinear ordinary differential equation (ODE) that accurately models the dynamics of a soft-helicoid trunk robot. We then use this ODE in a model predictive control scheme to demonstrate real-time precision control across various challenging …
@misc{kaundinya2026data,
title = {{Data-Driven Precision Control of Soft Helicoid Robots}},
author = {Roshan S. Kaundinya and Mark Leone and Roberto Sabella and Hugo Buurmeijer and Luis Pabon and John Irvin Alora and Marco Pavone and George Haller},
year = {2026},
eprint = {2611.11709},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11709},
}
Tendon-driven continuum robots can reach through constrained spaces, but contact along their bodies is invisible to the sensors at their motors. We show that this is a structural limitation rather than a tuning problem: the map from contact forces to tendon measurements has a null space of dimension at least seven, so no estimator that leaves the contact support free can locate contact. When the obstacle geometry is known, however, non-penetration and complementarity make the contact force locally identifiable, and we prove that the resulting system with unknown contact inputs is locally …
Justus Sagem\"uller, Harald Minde Hansen, Kristin Y. Pettersen
Abstract摘要
Constant- and piecewise-constant-curvature models are widely used to simplify the kinematics of continuum robots, although the assumed curvature profiles are not necessarily inherent mechanical properties of the robot and may therefore be violated during operation. Rather than assuming a prescribed curvature profile in the kinematic model and estimating deviations from it, this paper presents a multi-segment scissor-based support structure that mechanically constrains a continuum robot to exhibit prescribed constant or spatially varying curvature profiles. The design uses Taylor …
@misc{sagemuller2026curvature,
title = {{Curvature by Design: A Support Structure for Continuum Robots}},
author = {Justus Sagem\"uller and Harald Minde Hansen and Kristin Y. Pettersen},
year = {2026},
eprint = {2611.07091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07091},
}
Duo Tang, Ping Deng, Sophie Armanini, Yajing Shen et al.
Abstract摘要
Inspired by the coordination of global motion and local shape adaptation in snakes, elephant trunks, and octopus arms, we propose a unified planning and control framework for mobile continuum robots in cluttered and confined environments. Existing approaches often address high-level path planning and local motion control separately, limiting coordinated base-arm motion, kinematic consistency, and collision avoidance. Our framework integrates long-horizon trajectory planning with real-time geometric refinement under kinematic and environmental constraints. The global layer combines A* search …
@misc{tang2026riemannian,
title = {{Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments}},
author = {Duo Tang and Ping Deng and Sophie Armanini and Yajing Shen and Toshio Fukuda and Cosimo Della Santina and Peng Lu},
year = {2026},
eprint = {2611.06214},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.06214},
}
Reinhard M. Grassmann, Abdulaziz Y. Alkayas, Lingyun Chen, Sami Haddadin
Abstract摘要
High motion performance and avoiding structural vibrations rely on smooth trajectory planning. The smoothness requirement depends on the robot's complexity in terms of the number of degrees of freedom and system order due to the flexibility of the joints and links. For industrial robots, the smoothness requirements have been determined and subsequently taken into account. Although unknown for soft continuum robots, the exact smoothness requirement can potentially be much larger than previously considered ones such as $\mathbf{\mathcal{C}^4}$ for collaborative robots and …
@misc{grassmann2026mathcal,
title = {{$\mathcal{C}^\infty$ Smooth Trajectory Planning with Application to Soft Robots}},
author = {Reinhard M. Grassmann and Abdulaziz Y. Alkayas and Lingyun Chen and Sami Haddadin},
year = {2026},
eprint = {2611.05615},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05615},
}
Tony Tao, Jason Jingzhou Liu, Ruslan Salakhutdinov, Deepak Pathak
Abstract摘要
General-purpose mobile manipulation requires a combination of capable robot hardware, scalable demonstration collection, expressive whole-body control, and effective policy learning. Yet these components are often developed independently, creating mismatches that limit the capabilities of the complete system. We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation. Pixel combines a low-cost wheeled humanoid with human-aligned kinematics and compliant grippers, multiple teleoperation and robot-free human data-collection interfaces, a unified …
@misc{tao2026pixel,
title = {{Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation}},
author = {Tony Tao and Jason Jingzhou Liu and Ruslan Salakhutdinov and Deepak Pathak},
year = {2026},
eprint = {2611.03648},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03648},
}
Davide Costanzi, Eldison Dimo, Emanuele Feola, Mario Sedilesu et al.
Abstract摘要
Myoelectric Control Systems (MCSs) enable voluntary control of wearable robots through muscle activity. However, intention decoding, assistance shaping, and low-level actuator control are often not explicitly distinguished, making their contributions difficult to evaluate and compare. This review introduces a functional decoder-shaper-controller framework for continuous myoelectric control of upper-limb exoskeletons. The decoder estimates an intention-related variable from surface electromyography (sEMG), the shaper maps it to a control reference according to an assistance policy, and the …
@misc{costanzi2026myoelectric,
title = {{Myoelectric Control for Upper-Limb Exoskeletons: A Decoder-Shaper-Controller Framework, Design Guidelines, and Future Directions}},
author = {Davide Costanzi and Eldison Dimo and Emanuele Feola and Mario Sedilesu and Francesco Pascucci and Andrea Calanca},
year = {2026},
eprint = {2611.03418},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03418},
}
James Wood, André Farinha, Will Browne, Josh Pinskier et al.
Abstract摘要
Soft robot co-design methods can jointly optimise morphology and control, but the resulting designs are often difficult to manufacture, while manufacturable approaches typically restrict actuator placement or require manual assembly. We present a simulation-based co-design framework that jointly optimises material distribution, pneumatic actuator placement and orientation, and control, while producing designs that can be fabricated monolithically using fused deposition modelling. The framework uses a differentiable 2D plane-stress Material Point Method simulator, incorporating an …
@misc{wood2026towards,
title = {{Towards end-to-end co-design for one-shot printable task-specific soft robots}},
author = {James Wood and André Farinha and Will Browne and Josh Pinskier and Krishna Manaswi Digumarti},
year = {2026},
eprint = {2611.03216},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03216},
}
Haoyuan Wang, Shuai Peng, Yongkai Liao, Yuxiao Sun et al.
Abstract摘要
Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic ambiguity, and limited coordinated multisensory data. We introduce Soma, a dataset containing 100 objects, 1,000 continuous grasping trajectories, and 74,914 multimodal samples grouped by software acquisition cycle, spanning remote RGB, hand RGB, depth, tactile, IMU, and structured text observations. We also collect Soma-Success, an independent grasp-outcome dataset with 1,000 trials (998 retained after quality control), to evaluate frozen-encoder transfer for post-hoc grasp outcome …
@misc{wang2026somaalign,
title = {{SomaAlign: Reliability-Aware Multimodal Representation Learning for Soft Robotic Grasping}},
author = {Haoyuan Wang and Shuai Peng and Yongkai Liao and Yuxiao Sun and Wei Luo and Xinxing Chen and Jian Huang},
year = {2026},
eprint = {2611.02794},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02794},
}
Muyuan Ma, Shuo Jiang, Lijun Han, Xinpan Meng et al.
Abstract摘要
To reduce reliance on manual design and extensive parameter tuning, a framework for the autonomous evolution of exoskeleton assistance policies through human feedback is proposed. Assistance policies are autonomously generated as executable control code by an intelligent agent and evaluated in experiments with human participants. Subjective user feedback, movement performance, and muscle activation measurements are incorporated into policy assessment. Based on these assessments, policy deficiencies are autonomously identified and the control code is revised by the agent for subsequent trials. …
@misc{ma2026recursive,
title = {{Recursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback}},
author = {Muyuan Ma and Shuo Jiang and Lijun Han and Xinpan Meng and Zhenghua Ma and Xiuze Xia and Houcheng Li and Long Cheng},
year = {2026},
eprint = {2611.01581},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.01581},
}
Held-out validation loss is the standard offline signal for choosing checkpoints and comparing models, but its relation to closed-loop success on a real robot is rarely measured. We measure it for ACT, Diffusion Policy, SmolVLA and $\pi_{0.5}$ (action expert only), trained on nested subsets of 12 to 200 demonstrations of a pick-and-place task on an SO-101 arm. We score 82 checkpoints on 25 held-out demonstrations, with each policy's training loss and with the error of the actions it outputs, and compare the scores with 930 bench rollouts. We also score all 700 intermediate checkpoints of 70 …
Rajiv V. Joshi, Jimin An, Ilseung Park, Dawit Lee et al.
Abstract摘要
Data-driven controllers have enabled lower-limb robotic exoskeletons to adapt assistance to the user across many tasks. A leading example is biological torque control, which estimates the user's own joint moment from onboard sensors and assists in proportion to that estimate. However, its estimators are trained on data recorded with the target device, so deploying the controller on new hardware requires collecting new data. Existing open-source efforts release hardware, software, or controllers separately, so this data collection must still be repeated on the assembled device. Here, we …
@misc{joshi2026design,
title = {{Design and Validation of a Robotic Knee Exoskeleton with Musculoskeletal Simulation-Derived Biological Torque Control}},
author = {Rajiv V. Joshi and Jimin An and Ilseung Park and Dawit Lee and Inseung Kang},
year = {2026},
eprint = {2610.41981},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41981},
}
This work proposes a novel conveyor belt mechanism enabling continuous, active repositioning of functional elements along the body of a tendon-driven continuum robot. Up to three conveyor belt mechanisms are integrated within a 9 mm diameter continuum robot. We demonstrate equivalent or reduced conveyor belt actuation forces compared to robot tendon actuation forces. As a case study, we mount permanent magnets along two conveyor belts, and demonstrate actively reconfigurable inter-robot coupling. Coupling point location and quantity can be adjusted during robot operation to actively tune the …
@misc{walsh2026conveyor,
title = {{A Conveyor Belt Mechanism for Repositionable Magnetic Coupling Between Continuum Robots}},
author = {Ella Walsh and Eric Diller and Jessica Burgner-Kahrs},
year = {2026},
eprint = {2610.40600},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40600},
}
Jiahe Wang, Sultan Haidar Ali, Eric Weissman, Jiefeng Sun
Abstract摘要
Controlling continuum robots' shape to operate in confined, enclosed environments is challenging, since it cannot rely on external observation. Existing embedded shape sensing methods, although they can provide shape reconstruction, are insufficient to enable real-time shape control due to limitations in reconstruction accuracy, computational speed, and compatibility with shape-state representation. In this work, we present an embedded shape-sensing framework that uses readily available IMU and actuator feedback to reconstruct the continuum-robot shape with a neural network, providing …
@misc{wang2026controlling,
title = {{Controlling Continuum Robot Shape in Enclosed Space with Embedded Sensing using IMU and Actuator Feedback}},
author = {Jiahe Wang and Sultan Haidar Ali and Eric Weissman and Jiefeng Sun},
year = {2026},
eprint = {2610.39055},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39055},
}
Yifei Yuan, Jakob Wolf, Ghaith Androwis, Xianlian Zhou
Abstract摘要
Objective: This study aims to determine whether a hip-exoskeleton policy trained in simulation, by co-adapting with a musculoskeletal human model under a metabolic-energy reward, reduces the metabolic cost of walking in new users without per-user tuning, and how mechanical-power shaping affects the learned assistance. Methods: We developed MARL-Exo, a multi-agent reinforcement learning framework in which a full-body human model (304 muscle actuators; randomized anatomy, gait, and speed) and a bilateral hip-exoskeleton policy are trained concurrently in two stages. The exoskeleton observes …
@misc{yuan2026multi,
title = {{Multi-Agent Reinforcement Learning of Co-Adaptive Hip Exoskeleton Control (MARL-Exo) for Metabolic Energy Reduction}},
author = {Yifei Yuan and Jakob Wolf and Ghaith Androwis and Xianlian Zhou},
year = {2026},
eprint = {2610.37441},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37441},
}
We trained four widely used imitation-learning policies, ACT, Diffusion Policy, SmolVLA and $\pi_{0.5}$, on nested subsets of 12, 25, 50, 100 and 200 teleoperated demonstrations of one pick-and-place task with distractor objects, using one fixed training recipe per policy. Every trained model was then run on a physical SO-101 arm on a fixed set of object placements: 1,340 scored episodes in 126 blocks. ACT, trained from scratch, rose from 1 success in 40 episodes at 12 demonstrations to 37 in 40 at 100. $\pi_{0.5}$ with its action expert fine-tuned was the best policy when data was scarce (16 …
Lorenzo Ticozzi, Patricio A. Vela, Panagiotis Tsiotras
Abstract摘要
Spacecraft-mounted robotic manipulators can support long-term operability of orbiting assets by conducting tasks such as on-orbit assembly, repair, upgrade, and refueling. In this context, the integration of continuum/soft robotic mechanisms into space manipulator designs promises to reduce stowage volume and mass, as well as to improve operational safety through the inherent mechanical compliance of soft structures. This work presents a novel state reconstruction pipeline for a hybrid soft-rigid space manipulator, in which the distal link is a tendon-driven continuum robot. The estimation …
@misc{ticozzi2026state,
title = {{State estimation for a hybrid rigid-continuum space manipulator system}},
author = {Lorenzo Ticozzi and Patricio A. Vela and Panagiotis Tsiotras},
year = {2026},
eprint = {2610.37043},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37043},
}
Dixiang Wang, Minghao Zha, Arifur Rahman, Yinhong Qin et al.
Abstract摘要
Animals exhibit diverse shape-changing behaviors, from the undulatory swimming of eels to the arching movements of inchworms. Reproducing these biological shapes with modular soft robots offers a promising approach to developing versatile bioinspired robotic systems. However, translating a desired shape into a feasible robotic configuration remains challenging. Existing cable-driven modular soft robots rely on computationally expensive physics-based modeling and combinatorial search for inverse design, making it difficult to efficiently reproduce diverse target shapes. To address this …
@misc{wang2026from,
title = {{From Biological Curves to Soft Modular Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement}},
author = {Dixiang Wang and Minghao Zha and Arifur Rahman and Yinhong Qin and Yitao Jiang and Shiyang Lu and Zebing Mao and Muhao Chen and others},
year = {2026},
eprint = {2610.36681},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36681},
}
Tactile sensing provides robots with contact information difficult to obtain from vision alone. Shear force is particularly important for understanding tangential contact, guiding corrective motions, and maintaining grasp stability, yet flexible and low-cost tactile arrays often measure only normal force. We introduce VecTile, a low-cost, 6 mm thin, flexible tactile array capable of three-axis force sensing. VecTile combines a piezoresistive layer with passive silicone nubs that convert normal and shear forces into distinguishable pressure patterns. The sensor can be fabricated in-house …
@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},
}
Muyuan Ma, Long Cheng, Lijun Han, Haotian Zhai et al.
Abstract摘要
Exoskeletons have shown great potential for augmenting human physical capabilities, yet generating reliable assistance remains challenging because human motion patterns and interaction requirements vary substantially in practice. To address this problem, a dual-loop assistance strategy is proposed to couple multimodal exoskeleton trajectory generation with variable admittance control, where trajectory variance links the outer-loop generator and the inner-loop controller. The generator synthesizes future exoskeleton trajectories through denoising, primarily using whole-body human motion and …
@misc{ma2026whole,
title = {{Whole-Body Motion Guided Reference Generation for Uncertainty-Aware Compliant Assistance}},
author = {Muyuan Ma and Long Cheng and Lijun Han and Haotian Zhai and Xinpan Meng and Xiuze Xia and Houcheng Li and Zeyu Liu},
year = {2026},
eprint = {2610.31302},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31302},
}
Walking without vision requires timely, unambiguous guidance, yet many wearable aids encode directions as audio or vibrotactile signals that users must translate into movement. We present XoNav, a four-actuator hip exoskeleton that renders guidance cues directly at the hip joints. It renders an Advance cue and two-level Braking cues through sagittal-plane actuators and two-level Steering cues through frontal-plane actuators. A bilateral LSTM-based gait-state classifier uses two thigh-mounted IMUs to identify gait states, enabling haptic cues to be delivered in coordination with the wearer's …
@misc{sarkar2026xonav,
title = {{XoNav: An Exoskeleton as a Personalized Haptic Interface for Assisted Navigation}},
author = {Debadrata Sarkar and Soumen Sen and Aman Arora},
year = {2026},
eprint = {2610.27271},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27271},
}
Francesco Pascucci (University of Verona, Italy), Eldison Dimo (University of Verona, Italy) et al.
Abstract摘要
Electromyography (EMG)-driven control enables wearable robots to assist users based on their neuromuscular activity. Upper-limb exoskeletons typically rely on proportional myoelectric control (PMC), which amplifies user effort but may compromise stability at high assistance levels. EMG-driven adaptive gravity compensation (AGC) offers a promising alternative, counterbalancing gravitational torques while continuously adapting to neuromuscular activity. However, these approaches have not been systematically compared in a human-in-the-loop setting. This study evaluates five control algorithms: …
@misc{verona2026human,
title = {{A Human-in-the-Loop Comparison of Proportional Control and Adaptive Gravity Compensation in Elbow EMG-Driven Exoskeletons}},
author = {Francesco Pascucci (University of Verona and Italy) and Eldison Dimo (University of Verona and Italy) and Davide Costanzi (University of Verona and Italy) and Mario Sedilesu (University of Verona and Italy) and others},
year = {2026},
eprint = {2610.26445},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26445},
}
Jessica Gumowski, Julian Finn, Krishna Manaswi Digumarti, David Howard
Abstract摘要
Octopuses exhibit a powerful form of embodied intelligence from a highly adaptable soft body. One key mechanism is the ability to instantiate bending points at any location along the arm, which enables performant and dexterous behaviours. Taking inspiration from the octopus for its dexterity, this study focuses on changing the curvature of a tendon-actuated arm by adaptively changing the anchoring end-point along the arm. An array of inflatable shells is integrated into the arm, where inflating a shell causes an increase in friction at the location, jamming the tendon and creating a temporary …
Low-cost passive leader arms enable scalable data collection for imitation learning, but their lack of restoring forces leaves redundant joints unconstrained, yielding highly inconsistent joint-space trajectories. We address this with a low-cost passive joint module (an adjustable mechanical clutch and a 3D-printed spiral spring) that pulls each joint toward a user-defined neutral posture. This passive restoring force acts as a physical prior equivalent to joint-space $L_2$ regularization (``Hardware-as-Regularization''), improving kinematic consistency with shorter completion time than an …
Nitin Rai, Won Suk Lee, Hongyoung Leon, Heping Zhu
Abstract摘要
Precise pedicel-targeted grasping in strawberry harvesting remains a critical bottleneck due to the pedicel's small diameter (1.3-3 mm), color similarity with surrounding foliage, and precise force control required for clean abscission zone detachment. As a market-grade crop, strawberry aesthetic value makes bruise-free pedicel detachment essential over direct fruit grasping. Classical approaches struggle to unify vision models, motion planning, and contact manipulation into a robust framework, particularly in occluded canopy environments. Therefore, this work presents a visuomotor imitation …
@misc{rai2026pedicel,
title = {{Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm}},
author = {Nitin Rai and Won Suk Lee and Hongyoung Leon and Heping Zhu},
year = {2026},
eprint = {2610.22765},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.22765},
}
Yunti Xu, Aedan Mangan, Miheer Potdar, Alex Lu et al.
Abstract摘要
Continuum robots enable dexterous access to confined spaces in applications such as minimally invasive surgery and industrial inspection. However, their actuation systems are often custom-designed for specific robot architectures and tasks, limiting reuse across publications, institutions, and applications. This paper presents a unified actuation platform that decouples actuation hardware and software from continuum robot design. The platform provides modular actuation across continuum robot classes, scales to a large number of degrees of freedom (DOFs), and integrates with conventional …
@misc{xu2026design,
title = {{Design and Characterization of a Unified Continuum Robot Actuation Platform}},
author = {Yunti Xu and Aedan Mangan and Miheer Potdar and Alex Lu and Tania K. Morimoto},
year = {2026},
eprint = {2610.19798},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.19798},
}
Rajmeet Singh, Manveen Kaur, Yusuf Adamu, Irfan Hussain
Abstract摘要
Robotic harvesting of cherry tomatoes in greenhouses remains challenging due to dense fruit clusters, delicate skin, and the need for gentle yet effective detachment. This study presents a force-data-driven framework for autonomous cherry tomato harvesting that integrates six-axis force/torque sensing, a bio-inspired compliant gripper, and deployment on a mobile robotic platform. A wearable ATI six-axis force/torque sensor mounted on the index finger captured kinetic signatures of three elemental picking patterns (gripping, twisting, and lateral pulling) across 150 manual trials (50 per …