通过基于潜状态表征的策略切换拓展敏捷运动的通用性
Expanding Versatility of Agile Locomotion through Policy Transitions Using Latent State Representation
AI总结：
针对真实场景机器人运动通用性拓展问题，提出基于潜状态表征的transition-net鲁棒切换策略，可统一多运动策略实现任意策略对鲁棒切换，新增技能不影响旧技能，训练高效且切换成功率优于现有方法。
摘要：
本文提出了transition-net（切换网络），这是一种鲁棒的切换策略，可拓展真实场景中机器人运动的通用性。为此，我们首先将不同步态的复杂度分配到适用于真实机器人的专用运动策略中。接着，我们通过分析潜状态表征，将具备鲁棒切换能力的策略统一为单个连贯的元控制器，以此拓展机器人的通用性。该方法使机器人能够迭代拓展其技能库，并能在策略库中的任意策略对之间实现鲁棒切换。在我们的框架中，添加新技能不会引入任何改变已习得技能的流程。此外，单个消费级GPU训练一个运动策略耗时不到一小时。我们的方法在真实场景中有效，实验中针对最具挑战性的切换对，其平均成功率比现有方法高出19%。
英文摘要：
This paper proposes the transition-net, a robust transition strategy that expands the versatility of robot locomotion in the real-world setting. To this end, we start by distributing the complexity of different gaits into dedicated locomotion policies applicable to real-world robots. Next, we expand the versatility of the robot by unifying the policies with robust transitions into a single coherent meta-controller by examining the latent state representations. Our approach enables the robot to iteratively expand its skill repertoire and robustly transition between any policy pair in a library. In our framework, adding new skills does not introduce any process that alters the previously learned skills. Moreover, training of a locomotion policy takes less than an hour with a single consumer GPU. Our approach is effective in the real-world and achieves a 19% higher average success rate for the most challenging transition pairs in our experiments compared to existing approaches.
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。
