専門家方策と決定木を組み合わせたセンサベースロボットナビゲーション
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees
深層強化学習の専門家方策を決定木に変換し、再学習なしで修正することで、動的環境での衝突回避ナビゲーションの信頼性と性能を向上させた研究。
著者: Aaron M. Roth, Jing Liang, Dinesh Manocha
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments with moving obstacles or targets. Our approach uses deep reinforcement learning-based expert policy that is trained using a sim2real paradigm. In order to increase the reliability and handle the failure cases of the expert policy, we combine with a policy extraction technique to transform the resulting policy into a decision tree format. The resulting decision tree has properties which we use to analyze and modify the policy and improve performance on navigation metrics including smoothness, frequency of oscillation, frequency of immobilization, and obstruction of target. We are able to modify the policy to address these imperfections without retraining, combining the learning power of deep learning with the control of domain-specific algorithms. We highlight the benefits of our algorithm in simulated environments and navigating a Clearpath Jackal robot among moving pedestrians. (Videos at this url: https://gamma.umd.edu/researchdirections/xrl/navviper)
