论文标题
通过增强学习快速运动
Rapid Locomotion via Reinforcement Learning
论文作者
论文摘要
对于腿部机器人来说，诸如冲刺和高速转弯之类的敏捷演习都充满挑战。我们提出了一个端到端学习的控制器，该控制器可实现MIT Mini Cheetah的记录敏捷性，持续速度高达3.9 m/s。该系统在草，冰和砾石等自然地形上运行并快速转动，并对干扰的反应强烈。我们的控制器是通过强化学习对模拟进行培训的神经网络，并转移到现实世界中。这两个关键组成部分是（i）速度命令的自适应课程，以及（ii）一种在线系统识别策略，用于从先前工作中利用SIM到现实转移。机器人行为的视频可在以下网址提供：https：//agility.csail.mit.edu/
Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots. We present an end-to-end learned controller that achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9 m/s. This system runs and turns fast on natural terrains like grass, ice, and gravel and responds robustly to disturbances. Our controller is a neural network trained in simulation via reinforcement learning and transferred to the real world. The two key components are (i) an adaptive curriculum on velocity commands and (ii) an online system identification strategy for sim-to-real transfer leveraged from prior work. Videos of the robot's behaviors are available at: https://agility.csail.mit.edu/
