从翻身到行走：使类人机器人发展复杂运动技能
From Rolling Over to Walking: Enabling Humanoid Robots to Develop Complex Motor Skills
AI总结：
本文提出一种基于成就触发多路径奖励函数的强化学习方法，使类人机器人在单一训练阶段学习从翻身到行走的复杂运动技能，并在模拟环境中提升成功率与学习速度。
摘要：
本文提出了一种创新方法，使类人机器人通过强化学习获得全面的运动技能。该方法利用基于发展机器人学原理的成就触发多路径奖励函数，促进机器人在单一训练阶段内学习人类婴儿通常掌握的大运动技能。在模拟环境中，所提方法在成功率和学习速度上优于标准强化学习技术。通过利用婴儿学习所固有的自我发现和探索原则，该方法有望显著推进类人机器人运动技能的获取。
英文摘要：
This paper presents an innovative method for humanoid robots to acquire a comprehensive set of motor skills through reinforcement learning. The approach utilizes an achievement-triggered multi-path reward function rooted in developmental robotics principles, facilitating the robot to learn gross motor skills typically mastered by human infants within a single training phase. The proposed method outperforms standard reinforcement learning techniques in success rates and learning speed within a simulation environment. By leveraging the principles of self-discovery and exploration integral to infant learning, this method holds the potential to significantly advance humanoid robot motor skill acquisition.
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。
