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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 13 Pith papers
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FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
FTP-1 is the first foundation tactile policy pretrained on ~3000 hours of data from 26 sources across 21 sensors that improves performance on seen setups by 17.2% and transfers to unseen sensors with 31% success rate gain.
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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
Wrist-guided whole-body RL retargets human–object interactions without finger pose supervision, matching supervised methods and generalizing across hand morphologies in simulation.
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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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Tac-DINO: Learning Vision-Tactile Features with Patch Alignment
Tac-DINO constructs a large tactile dataset and Vis-Tac Holographic Matching Benchmark, then proposes Vision-Tactile Patch Alignment (VTPA) methods that outperform non-aligned baselines on local-to-global feature matching.
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Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
MiTaS fuses multi-resolution tactile data from GelSight and Evetac sensors with vision using modality-specific stems and transformer fusion to condition flow-matching policies, reporting 80% average success on five co...
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Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy trained from human demos and sim RL improves real quadrupedal loco-manipulation by 28.54% on average over vision-only and visuotactile baselines.
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Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A tactile-aware hierarchical policy for quadrupedal loco-manipulation improves real-world contact-rich task performance by 28.54% over vision-only and visuotactile baselines.
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ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success an...
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Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
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Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy combines human-demonstration training for contact cue prediction with sim-to-real reinforcement learning to improve quadrupedal loco-manipulation performance by 28.54% over vision b...
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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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