Touch Where It Matters: Learning Local Tactile Policies for Fine-Grained Gripper Control
Visual imitation policies can organise task motion, yet contact-rich object use demands gripper adjustments beyond coarse opening and closing.
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Key points
- We study fine-grained gripper control as a local feedback problem within ongoing task execution.
- We introduce ContactPolicy, a learned local controller that takes over the gripper while a visual base policy continues to control the arm.
- ContactPolicy combines tactile and force histories with current robot state to jointly predict gripper increments and their modulation, updating the gripper target from each new observation.
- These results support task-local tactile policies for fine-grained gripper control within visual manipulation.
Sources (1)
- [1]Touch Where It Matters: Learning Local Tactile Policies for Fine-Grained Gripper ControlarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 06:10 AM
Visual imitation policies can organise task motion, yet contact-rich object use demands gripper adjustments beyond coarse opening and closing.
We study fine-grained gripper control as a local feedback problem within ongoing task execution.
Extractive summary: sentences quoted from the sources.
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