Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Tairan He Chong Zhang Wenli Xiao Guanqi He Changliu Liu Guanya Shi
Legged robots navigating cluttered environments must be jointly agile for
efficient task execution and safe to avoid collisions with obstacles or humans.
Existing studies either develop conservative controllers (< 1.0 m/s) to ensure
safety, or focus on agility without considering potentially fatal collisions.
This paper introduces Agile But Safe (ABS), a learning-based control framework
that enables agile and collision-free locomotion for quadrupedal robots. ABS
involves an agile policy to execute agile motor skills amidst obstacles and a
recovery policy to prevent failures, collaboratively achieving high-speed and
collision-free navigation. The policy switch in ABS is governed by a learned
control-theoretic reach-avoid value network, which also guides the recovery
policy as an objective function, thereby safeguarding the robot in a closed
loop. The training process involves the learning of the agile policy, the
reach-avoid value network, the recovery policy, and an exteroception
representation network, all in simulation. These trained modules can be
directly deployed in the real world with onboard sensing and computation,
leading to high-speed and collision-free navigation in confined indoor and
outdoor spaces with both static and dynamic obstacles.