  Recent advancements in optimal control and reinforcement learning have
enabled quadrupedal robots to perform various agile locomotion tasks over
diverse terrains. During these agile motions, ensuring the stability and
resiliency of the robot is a primary concern to prevent catastrophic falls and
mitigate potential damages. Previous methods primarily focus on recovery
policies after the robot falls. There is no active safe falling solution to the
best of our knowledge. In this paper, we proposed Guardians as You Fall (GYF),
a safe falling/tumbling and recovery framework that can actively tumble and
recover to stable modes to reduce damage in highly dynamic scenarios. The key
idea of GYF is to adaptively traverse different stable modes via active
tumbling before the robot shifts to irrecoverable poses. Via comprehensive
simulation and real-world experiments, we show that GYF significantly reduces
the maximum acceleration and jerk of the robot base compared to the baselines.
In particular, GYF reduces the maximum acceleration and jerk by 20%~73% in
different scenarios in simulation and real-world experiments. GYF offers a new
perspective on safe falling and recovery in locomotion tasks, potentially
enabling much more aggressive explorations of existing agile locomotion skills.
