学习四足机器人穿越受限空间的高动态技能转换
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
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中文总结 AI 辅助
该研究提出分层强化学习框架，结合模仿学习训练低级技能、高级控制器视觉感知规划轨迹，实现四足机器人自主敏捷穿越狭窄门，还可扩展至其他高动态任务。
中文摘要 AI 辅助
尽管腿式动物能够在穿越狭窄空间时完成爆发性动作，但在四足机器人上复现该行为一直是长期挑战。本文提出一种分层强化学习流程，使机器人能通过狭窄门这类受限障碍物完成激进运动。采用模仿学习技术训练低级策略，模仿真实动物行为并形成多样技能集；高级控制器知晓低级技能能力，通过视觉检测获取门的信息，确定合适的无碰撞轨迹以动态穿越。值得注意的是，该框架可扩展至其他高动态任务，这是首批在地面行走机器人上完成自主敏捷空中穿门任务的研究之一，将腿式机器人的类生敏捷性提升至与生物 counterparts 相当的水平。
英文摘要
Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challenge. Here, we propose a hierarchical reinforcement learning pipeline that empowers the robots to perform aggressive locomotion through constrained obstacles--a narrow gate. The imitation learning technique is used to train the low-level policy, which mimics the behaviors of real animals and forms a set of diverse skills. The high-level controller, having an awareness of the capability of low-level skills and acquiring the gate information via vision-based detection, determines the suitable maneuvers with collision-free trajectories to traverse it dynamically. Notably, we also verify that this framework can be extended to other highly dynamic tasks. This is one of the first works that perform autonomous and agile aerial gate traversal tasks on ground-walking robots, extending the lifelike agility of legged robots to match that of their biological counterparts.
发表机构
- University of Hong Kong（香港大学）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
