通过多任务学习进行基础策略获取以生成运动技能
Foundational Policy Acquisition via Multitask Learning for Motor Skill Generation
AI总结：
本研究提出一种用于基础策略获取的多任务强化学习算法，通过构建编码器-解码器网络及网络选择机制，使策略能适应多变的动态运动任务，并成功生成全新的运动技能。
摘要：
在本研究中，我们提出了一种用于基础策略获取的多任务强化学习算法，以生成新颖的运动技能。在动态运动生成任务中，学习多任务策略的丰富表征是一项挑战，因为策略需要应对具有不同奖励函数或物理参数的目标或环境变化。受人类感觉运动适应机制的启发，我们开发了学习流程来构建编码器-解码器网络和网络选择，以促进在多种情况下获取基础策略。首先，我们在标准多运动任务中将所提出的方法与先前的多任务强化学习方法进行了比较。结果表明，所提出的方法优于基线方法。然后，我们使用单足机器人模型将所提出的方法应用于顶球任务，以评估技能生成性能。结果表明，所提出的方法能够适应新颖的目标位置或未经历过的球恢复系数，并且能够获取最初为顶球动作学习的基础策略网络，该网络可以生成全新的过头踢球技能。
英文摘要：
In this study, we propose a multitask reinforcement learning algorithm for foundational policy acquisition to generate novel motor skills. \textcolor{\hcolor}{Learning the rich representation of the multitask policy is a challenge in dynamic movement generation tasks because the policy needs to cope with changes in goals or environments with different reward functions or physical parameters. Inspired by human sensorimotor adaptation mechanisms, we developed the learning pipeline to construct the encoder-decoder networks and network selection to facilitate foundational policy acquisition under multiple situations. First, we compared the proposed method with previous multitask reinforcement learning methods in the standard multi-locomotion tasks. The results showed that the proposed approach outperformed the baseline methods. Then, we applied the proposed method to the ball heading task using a monopod robot model to evaluate skill generation performance. The results showed that the proposed method was able to adapt to novel target positions or inexperienced ball restitution coefficients but to acquire a foundational policy network, originally learned for heading motion, which can generate an entirely new overhead kicking skill.
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。
