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arXiv · 1912.00745

Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor

Chen Lu·Jing Wang·Shan Luo
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Abstract

Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks. One of the primitive but important tasks is surface following that is a key feature for robots while exploring unknown environments or workspace of inaccurate modeling. In this paper, we propose a novel end-to-end learning strategy, by directly mapping the raw tactile data acquired from a GelSight tactile sensor to the motion of the robot end-effector.Experiments on a KUKA youBot platform equipped with theGelSight sensor show that 80% of the actions generated by a fully trained SFDQN model are proper surface following actions; the autonomous surface following test also indicates that the proposed solution works well on a test surface.

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Chen Lu, Jing Wang, Shan Luo. 2019-12-02. Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor. https://arxiv.org/abs/1912.00745

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