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MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation
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Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided policy from teleoperated demonstration data. However, to provide the demonstration, human users often rely on visual feedback to control the robot. This creates a gap between the sensing modality used for controlling the robot (visual) and the modality of interest (tactile). To bridge this gap, we introduce "MimicTouch", a novel framework for learning policies directly from demonstrations provided by human users with their hands. The key innovations are i) a human tactile data collection system which collects multi-modal tactile dataset for learning human's tactile-guided control strategy, ii) an imitation learning-based framework for learning human's tactile-guided control strategy through such data, and iii) an online residual RL framework to bridge the embodiment gap between the human hand and the robot gripper. Through comprehensive experiments, we highlight the efficacy of utilizing human's tactile-guided control strategy to resolve contact-rich manipulation tasks. The project website is at https://sites.google.com/view/MimicTouch.
Forward citations
Cited by 7 Pith papers
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Feel the Force: Contact-Driven Learning from Humans
FeelTheForce trains a robot policy on human tactile demonstrations, predicting desired contact forces and using a PD controller to track them on the robot gripper, achieving 77% success across five force-sensitive tasks.
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LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing
LocoTouch trains a quadrupedal policy that uses a 221-taxel tactile back to balance and transport unsecured cylindrical objects, transferring zero-shot to a real Unitree Go1.
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TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
A tactile-rich HOI dataset plus a tri-component force reward improves contact fidelity and success of human-to-robot dexterous transfer over kinematic imitation alone.
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WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation
WristMimic achieves comparable or superior object manipulation retargeting by supervising wrist kinematics while letting finger behavior emerge from object and contact dynamics.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.
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ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation
A co-training framework that maps retargeted human hand trajectories to robot demonstrations with dynamic time warping and MixUp interpolation improves robot manipulation success rates and smoothness across four embodiments.
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GelFusion: Enhancing Robotic Manipulation under Visual Constraints via Visuotactile Fusion
A vision-led cross-attention fusion of camera and GelSight touch images improves imitation-learning success rates on three contact-rich manipulation tasks.
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