Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World

Understand

Legged robots are physically capable of traversing a wide range of challenging environments, but designing controllers that are sufficiently robust to handle this diversity has been a long-standing challenge in robotics.

• Reinforcement learning presents an appealing approach for automating the controller design process and has been able to produce remarkably robust controllers when trained in a suitable range of environments.
• However, it is difficult to predict all likely conditions the robot will encounter during deployment and enumerate them at training-time.
• What if instead of training controllers that are robust enough to handle any eventuality, we enable the robot to continually learn in any setting it finds itself in? This kind of real-world reinforcement learning poses a number of challenges, including efficiency, safety, and autonomy.