Legged robots have enormous potential in their range of capabilities, from
navigating unstructured terrains to high-speed running. However, designing
robust controllers for highly agile dynamic motions remains a substantial
challenge for roboticists. Reinforcement learning (RL) offers a promising
data-driven approach for automatically training such controllers. However,
exploration in these high-dimensional, underactuated systems remains a
significant hurdle for enabling legged robots to learn performant,
naturalistic, and versatile agility skills. We propose a framework for training
complex robotic skills by transferring experience from existing controllers to
jumpstart learning new tasks. To leverage controllers we can acquire in
practice, we design this framework to be flexible in terms of their source --
that is, the controllers may have been optimized for a different objective
under different dynamics, or may require different knowledge of the
surroundings -- and thus may be highly suboptimal for the target task. We show
that our method enables learning complex agile jumping behaviors, navigating to
goal locations while walking on hind legs, and adapting to new environments. We
also demonstrate that the agile behaviors learned in this way are graceful and
safe enough to deploy in the real world.

这篇论文研究了如何用增强学习算法训练四肢机器人在复杂环境中实现高速奔跑、跳跃、走路到目的地等多种复杂技能，并且通过从已有的控制器中借鉴经验，解决了机器人探索新环境时的困难，实现了四肢机器人在现实环境中优雅且安全的部署。