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MimicTouch:利用多模式人类触觉演示实现丰富的接触式操作
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https://www.youtube.com/watch?v=RWHoZOUtQvg&t=12s MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation Abstract Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. An effective strategy to learn tactile-guided policy is to train neural networks that map tactile signal to control based on teleoperated demonstration data. However, to provide the teleoperated 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. https://sites.google.com/view/MimicTouch https://lab-idar.gatech.edu/wp-content/uploads/2024/06/MimicTouch_CoRL_2024.pdf
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