関節クランプを用いた移動マニピュレータの強化学習協調制御の改善
Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping
LIDARで障害物を回避しつつ台車とマニピュレータを全身協調制御する強化学習エージェントに対し、関節ペナルティと関節限界の扱いを改善し、ベースラインより高い成功率と全身協調が必須な環境の解決を実現した。
著者: Denis Hadjivelichkov, Kostas Vlachos, Dimitrios Kanoulas
分類: cs.RO
原文アブストラクト
Many robotic path planning problems are continuous, stochastic, and high-dimensional. The ability of a mobile manipulator to coordinate its base and manipulator in order to control its whole-body online is particularly challenging when self and environment collision avoidance is required. Reinforcement Learning techniques have the potential to solve such problems through their ability to generalise over environments. We study joint penalties and joint limits of a state-of-the-art mobile manipulator whole-body controller that uses LIDAR sensing for obstacle collision avoidance. We propose directions to improve the reinforcement learning method. Our agent achieves significantly higher success rates than the baseline in a goal-reaching environment and it can solve environments that require coordinated whole-body control which the baseline fails.
