在具有避障功能的挑战性环境中学习人形机器人的双足行走
Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
AI总结：
本文提出一种基于策略的强化学习方法，通过在先进奖励函数中增加距离奖励项，成功实现了人形机器人在有障碍物环境中的双足行走与避障导航。
摘要：
深度强化学习在人形机器人上实现动态行走方面已有成功应用。然而，这些应用迄今为止仅在无障碍物的简单环境中取得成功。本文旨在使用基于策略的强化学习，在存在障碍物的环境中实现双足运动。通过在能实现基本双足运动的先进奖励函数中加入简单的距离奖励项，训练出的策略成功引导机器人驶向目标目的地，且沿途未与障碍物发生碰撞。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
