視覚誘導による難地形での動的歩行の学習
Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains
LiDAR知覚を用いた深層強化学習で、部分的観測環境下でも難地形を能動的に踏破する歩行ポリシーを学習させた研究。動的報酬戦略により履歴データ不要で多様な歩容を獲得し、90%以上の成功率を達成。
著者: Zhaocheng Liu, Fernando Acero, Zhibin Li
分類: cs.RO
原文アブストラクト
Legged robots are becoming increasingly powerful and popular in recent years for their potential to bring the mobility of autonomous agents to the next level. This work presents a deep reinforcement learning approach that learns a robust Lidar-based perceptual locomotion policy in a partially observable environment using Proximal Policy Optimisation. Visual perception is critical to actively overcome challenging terrains, and to do so, we propose a novel learning strategy: Dynamic Reward Strategy (DRS), which serves as effective heuristics to learn a versatile gait using a neural network architecture without the need to access the history data. Moreover, in a modified version of the OpenAI gym environment, the proposed work is evaluated with scores over 90% success rate in all tested challenging terrains.
