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.
Fang et al.,DEXOP: A device for robotic transfer of dexterous human manipulation
7 Pith papers cite this work. Polarity classification is still indexing.
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DexJoCo is a benchmark and toolkit with 11 functionally grounded tasks, 1.1K trajectories, and empirical benchmarks for task-oriented dexterous manipulation on MuJoCo.
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.
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% task completion and lower perceived workload than separated setups.
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.
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.
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 as paper singulation and orange peeling.
citing papers explorer
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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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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% task completion and lower perceived workload than separated setups.
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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 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 as paper singulation and orange peeling.