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DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
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DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
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We introduce perioperation, a paradigm for robotic data collection that sensorizes and records human manipulation while maximizing the transferability of the data to real robots. We implement this paradigm in DEXOP, a passive hand exoskeleton designed to maximize human ability to collect rich sensory (vision + tactile) data for diverse dexterous manipulation tasks in natural environments. DEXOP mechanically connects human fingers to robot fingers, providing users with direct contact feedback (via proprioception) and mirrors the human hand pose to the passive robot hand to maximize the transfer of demonstrated skills to the robot. The force feedback and pose mirroring make task demonstrations more natural for humans compared to teleoperation, increasing both speed and accuracy. We evaluate DEXOP across a range of dexterous, contact-rich tasks, demonstrating its ability to collect high-quality demonstration data at scale. Policies learned with DEXOP data significantly improve task performance per unit time of data collection compared to teleoperation, making DEXOP a powerful tool for advancing robot dexterity. Our project page is at https://dex-op.github.io.
Forward citations
Cited by 16 Pith papers
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Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
Force Policy learns a global vision policy for free space and a local force-feedback policy that recovers an interaction frame to execute stable hybrid force-position control in contact-rich manipulation.
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DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection
A hybrid kinesthetic-arm-plus-webcam-hand teleoperation interface achieved 17x/3x higher demonstration throughput than vision baselines and trained a 90%-success pick-and-place policy in a ten-person study.
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Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion
LAG-Fusion composes asynchronous diffusion policies by rebasing delayed guidance into the current action frame and fusing with latency-aware weights, improving contact-rich manipulation performance.
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Towards Human-level Dexterous Teleoperation
A single-stage RL co-tracking controller trained on consecutive human-derived hand–object subgoals achieves ~75% real-robot success on long-horizon dexterous teleoperation where baselines fail.
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Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Task-agnostic RL play pretraining on diverse objects yields a reusable dexterous prior that makes sparse-reward assembly learning ~33× more sample-efficient and enables zero-shot sim-to-real transfer on tight insertio...
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Transferring Contact, Not Just Motion: Compliant Grasping Across Dexterous Hands
A cross-embodiment force-position interface with system-identified torque calibration enables a flow-matching policy to perform transferable compliant grasping on heterogeneous dexterous hands.
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RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning
A wearable interface with a shared dexterous hand module enables retargeting-free teleoperation and matched data collection, yielding policies with 88.75% average success across eight real-robot tasks that generalize ...
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DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
DexJoCo is a benchmark and toolkit with 11 functionally grounded tasks, 1.1K trajectories, and empirical benchmarks for task-oriented dexterous manipulation on MuJoCo.
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FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception
FingerViP equips each finger with a miniature camera and trains a multi-view diffusion policy that achieves 80.8% success on real-world dexterous tasks previously limited by wrist-camera occlusion.
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DEX-Mouse: A Low-cost Portable and Universal Interface with Force Feedback for Data Collection of Dexterous Robotic Hands
DEX-Mouse is a portable, calibration-free teleoperation interface under $150 with kinesthetic force feedback that supports mounting the robot hand on the operator's forearm for aligned data collection, achieving 86.67...
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ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration
ActiveGlasses learns robot manipulation from ego-centric human demos captured with active vision via smart glasses, achieving zero-shot transfer using object-centric point-cloud policies.
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HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
HandelBot achieves precise bimanual piano playing by refining a simulation policy through lateral finger adjustments and residual RL, outperforming direct sim deployment by 1.8x with only 30 minutes of physical data a...
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AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance
Self-supervised fingertip mapping with few-shot human anchors and a pinch contact classifier yields calibration-free, more intuitive retargeting across diverse human-like robot hands.
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Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Play2Perfect uses task-agnostic RL play pretraining on diverse objects to build reusable manipulation priors, then fine-tunes for assembly, yielding 33x sample efficiency gains and 60% success on 0.5mm-clearance inser...
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HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
HandelBot refines simulation policies via physical rollouts and residual RL to achieve precise bimanual piano playing, outperforming direct sim transfer by 1.8x with only 30 minutes of real data across five songs.
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PLATO Hand: Shaping Contact Behavior with Fingernails for Precise Manipulation
The PLATO Hand introduces a hybrid fingertip with rigid fingernail and compliant pulp, guided by a strain-energy bending-indentation model, to achieve improved pinch stability and success in edge-sensitive tasks such ...
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