Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor–Critic with Hindsight Experience Replay

Sensors

Jae-Han Park¹  Ji-Hun Bae¹  Evan Prianto²  Jung-Su Kim²  MyeongSeop Kim² 

[1] Applied Robot R&D Department, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Korea;Department of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul 01811, Korea;

关键词: path planning;    multi-arm manipulators;    reinforcement learning;    Soft Actor-Critic (SAC);    Hindsight Experience Replay (HER);    collision avoidance;   

DOI  :  10.3390/s20205911

来源: DOAJ

【 摘 要 】

Since path planning for multi-arm manipulators is a complicated high-dimensional problem, effective and fast path generation is not easy for the arbitrarily given start and goal locations of the end effector. Especially, when it comes to deep reinforcement learning-based path planning, high-dimensionality makes it difficult for existing reinforcement learning-based methods to have efficient exploration which is crucial for successful training. The recently proposed soft actor–critic (SAC) is well known to have good exploration ability due to the use of the entropy term in the objective function. Motivated by this, in this paper, a SAC-based path planning algorithm is proposed. The hindsight experience replay (HER) is also employed for sample efficiency and configuration space augmentation is used in order to deal with complicated configuration space of the multi-arms. To show the effectiveness of the proposed algorithm, both simulation and experiment results are given. By comparing with existing results, it is demonstrated that the proposed method outperforms the existing results.

【 授权许可】

Unknown