GelSight触覚センサと深層強化学習による表面追従
Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor
GelSight触覚センサの生データからエンドエフェクタの動きを直接学習する深層強化学習手法を提案し、KUKA youBotで表面追従を実現した。
著者: Chen Lu, Jing Wang, Shan Luo
分類: cs.RO
原文アブストラクト
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.
