Acrobotics：四足机器人跑酷的通用方法
Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour
AI总结：
提出一种用于四足机器人的通用强化学习算法，在动态运动场景下，其策略性能媲美混合专家方法训练的最先进专家策略，且训练智能体数量仅需25%。
摘要：
攀爬、蹲伏、跨越间隙和上楼梯只是四足机器人相对于轮式机器人的部分优势，使其更适合在崎岖和非结构化地形中导航。然而，执行此类机动动作需要精确的时间协调和复杂的智能体-环境交互。此外，腿部运动本质上更容易发生滑动和绊倒，因此通过建模这些情况来设计鲁棒控制器的经典方法很快变得不切实际。相比之下，强化学习通过试错实现最优控制，提供了一种极具吸引力的解决方案。我们提出了一种用于动态运动场景中四足智能体的通用强化学习算法。所学习的策略可与使用混合专家方法训练的最先进的专家策略相媲美，而在训练期间仅使用25%的智能体。我们的实验还突出了通用运动策略的关键组成部分及其成功的主要因素。
英文摘要：
Climbing, crouching, bridging gaps, and walking up stairs are just a few of the advantages that quadruped robots have over wheeled robots, making them more suitable for navigating rough and unstructured terrain. However, executing such manoeuvres requires precise temporal coordination and complex agent-environment interactions. Moreover, legged locomotion is inherently more prone to slippage and tripping, and the classical approach of modeling such cases to design a robust controller thus quickly becomes impractical. In contrast, reinforcement learning offers a compelling solution by enabling optimal control through trial and error. We present a generalist reinforcement learning algorithm for quadrupedal agents in dynamic motion scenarios. The learned policy rivals state-of-the-art specialist policies trained using a mixture of experts approach, while using only 25% as many agents during training. Our experiments also highlight the key components of the generalist locomotion policy and the primary factors contributing to its success.
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。
