Quadruped locomotion now has acquired the skill to traverse or even sprint on uneven terrains in remote uncontrolled environment. However, surviving in the wild requires not only the maneuverability, but also the ability to handle unexpected hardware failures. We present the first deep reinforcement learning based methodology to train fault-tolerant controllers, which can bring an injured quadruped back home safely and speedily. We adopt the teacher-student 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 simulation and real-world experiments demonstrate that our fault-tolerant controller can efficiently lead a quadruped stably when it faces joint failure during locomotion.
翻译:四重移动现已获得在偏远不受控制的环境中穿越甚至冲破不均的地形的技能。然而,野外生存不仅需要机动性,还需要处理意外硬件故障的能力。我们提出了第一种深强化学习方法,用于培训容错控制器,可以安全、迅速地将受伤的四重伤者安全地带回家中。我们采用了师生框架,在模拟中将控制器训练为接近现实的连锁故障,这可以是零弹,不作任何微调即可转移到物理机器人。广泛的模拟和现实世界实验表明,当我们的容错控制器在移动期间面临联合故障时,它可以有效地导致四重刺。