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arXiv · 2109.04322

Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains

Zhaocheng Liu·Fernando Acero·Zhibin Li
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Abstract

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.

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Zhaocheng Liu, Fernando Acero, Zhibin Li. 2021-09-09. Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains. https://arxiv.org/abs/2109.04322

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