  Adaptive falling and recovery skills greatly extend the applicability of
robot deployments. In the case of legged mobile manipulators, the robot arm
could adaptively stop the fall and assist the recovery. Prior works on falling
and recovery strategies for legged mobile manipulators usually rely on
assumptions such as inelastic collisions and falling in defined directions to
enable real-time computation. This paper presents a learning-based approach to
reducing fall damage and recovery. An asymmetric actor-critic training
structure is used to train a time-invariant policy with time-varying reward
functions. In simulated experiments, the policy recovers from 98.9\% of initial
falling configurations. It reduces base contact impulse, peak joint internal
forces, and base acceleration during the fall compared to the baseline methods.
The trained control policy is deployed and extensively tested on the ALMA robot
hardware. A video summarizing the proposed method and the hardware tests is
available at https://youtu.be/avwg2HqGi8s.
