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.
@article{arxiv.2109.04322,
title = {Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains},
author = {Zhaocheng Liu and Fernando Acero and Zhibin Li},
journal= {arXiv preprint arXiv:2109.04322},
year = {2021}
}