拯救跛行者：通过强化学习实现容错四足运动
Saving the Limping: Fault-tolerant Quadruped Locomotion via Reinforcement Learning
AI总结：
针对四足机器人关节锁死等硬件故障，本文采用 teacher-student 强化学习框架在仿真中训练容错运动控制器，并实现无需微调的零样本真机迁移。
摘要：
现代四足机器人能够在远程非受控环境中熟练穿越甚至冲刺不平坦地形。然而，在野外生存不仅需要机动性，还需要应对潜在严重硬件故障的能力。如何赋予四足机器人这种能力，目前很少得到研究。本文提出了一种新颖方法，用于在仿真和物理世界中训练并测试四足运动的硬件容错控制器。我们采用 teacher-student 强化学习框架，在仿真中利用接近真实的关节锁死故障训练控制器，该控制器无需任何微调即可零样本迁移到物理机器人。大量实验表明，当四足机器人在运动过程中面临关节故障时，我们的容错控制器能够高效引导其保持稳定。
英文摘要：
Modern quadrupeds are skillful in traversing or even sprinting on uneven terrains in a remote uncontrolled environment. However, survival in the wild requires not only maneuverability, but also the ability to handle potential critical hardware failures. How to grant such ability to quadrupeds is rarely investigated. In this paper, we propose a novel methodology to train and test hardware fault-tolerant controllers for quadruped locomotion, both in the simulation and physical world. We adopt the teacher-student reinforcement learning framework to train the controller with close-to-reality joint-locking failure in the simulation, which can be zero-shot transferred to the physical robot without any fine-tuning. Extensive experiments show that our fault-tolerant controller can efficiently lead a quadruped stably when it faces joint failures during locomotion.
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。
