次元可変ロボットのための深層強化学習による地図なしナビゲーション
Dimension-variable Mapless Navigation with Deep Reinforcement Learning
メタロボットで学習したナビゲーション技能を観測のスケーリングにより異なる寸法のロボットへ転移し、再学習なしで未知・動的環境を走行可能にした。
著者: Wei Zhang, Yunfeng Zhang, Ning Liu, Kai Ren
分類: cs.RO
原文アブストラクト
Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained agents are limited to the specific robot dimensions used during training, hindering their applicability when the robot's dimension changes for task-specific requirements. To overcome this limitation, we propose a dimension-variable robot navigation method based on DRL. Our approach involves training a meta agent in simulation and subsequently transferring the meta skill to a dimension-varied robot using a technique called dimension-variable skill transfer (DVST). During the training phase, the meta agent for the meta robot learns self-navigation skills with DRL. In the skill-transfer phase, observations from the dimension-varied robot are scaled and transferred to the meta agent, and the resulting control policy is scaled back to the dimension-varied robot. Through extensive simulated and real-world experiments, we demonstrated that the dimension-varied robots could successfully navigate in unknown and dynamic environments without any retraining. The results show that our work substantially expands the applicability of DRL-based navigation methods, enabling them to be used on robots with different dimensions without the limitation of a fixed dimension. The video of our experiments can be found in the supplementary file.
