  Designing control policies for legged locomotion is complex due to the
under-actuated and non-continuous robot dynamics. Model-free reinforcement
learning provides promising tools to tackle this challenge. However, a major
bottleneck of applying model-free reinforcement learning in real world is
safety. In this paper, we propose a safe reinforcement learning framework that
switches between a safe recovery policy that prevents the robot from entering
unsafe states, and a learner policy that is optimized to complete the task. The
safe recovery policy takes over the control when the learner policy violates
safety constraints, and hands over the control back when there are no future
safety violations. We design the safe recovery policy so that it ensures safety
of legged locomotion while minimally intervening in the learning process.
Furthermore, we theoretically analyze the proposed framework and provide an
upper bound on the task performance. We verify the proposed framework in four
locomotion tasks on a simulated and real quadrupedal robot: efficient gait,
catwalk, two-leg balance, and pacing. On average, our method achieves 48.6%
fewer falls and comparable or better rewards than the baseline methods in
simulation. When deployed it on real-world quadruped robot, our training
pipeline enables 34% improvement in energy efficiency for the efficient gait,
40.9% narrower of the feet placement in the catwalk, and two times more jumping
duration in the two-leg balance. Our method achieves less than five falls over
the duration of 115 minutes of hardware time.
