让机器人在有障碍物的环境中学会不撞墙地走路
Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
- 用强化学习策略，通过距离奖励引导机器人避障
- 训练出的策略能安全到达目标，且不与障碍物碰撞
- 适合研究复杂环境下的机器人行走控制
深度强化学习已在简单环境下成功实现人形机器人的动态行走。然而，现有方法多局限于无障碍场景。本文采用基于策略的强化学习，在先进奖励函数基础上增加简单的距离奖励项，使训练出的策略能在存在障碍物的环境中，引导机器人安全抵达目标位置，避免与障碍物发生碰撞。
原文摘要 · Abstract (English)
Deep reinforcement learning has seen successful implementations on humanoid robots to achieve dynamic walking. However, these implementations have been so far successful in simple environments void of obstacles. In this paper, we aim to achieve bipedal locomotion in an environment where obstacles are present using a policy-based reinforcement learning. By adding simple distance reward terms to a state of art reward function that can achieve basic bipedal locomotion, the trained policy succeeds in navigating the robot towards the desired destination without colliding with the obstacles along the way.
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