Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
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.
65bb0c77e3ffc2ba202979df
Tairan He
claimed_verified
2024-02-02T09:01:26.014Z
65bc04ca719492167d710847
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uW8Cqff2XyFYJCKWHlgO4.png
Tairan He
user
tairanhe
65bb0c77e3ffc2ba202979e0
Chong Zhang
65bb0c77e3ffc2ba202979e1
Wenli Xiao
65bb0c77e3ffc2ba202979e2
Guanqi He
65bb0c77e3ffc2ba202979e3
Changliu Liu
65bb0c77e3ffc2ba202979e4
Guanya Shi
claimed_verified
2024-02-02T09:01:28.063Z
65bbcbadadc958a7d9ef8914
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/9Pv8b1nUXIP2fvTS281DK.jpeg
Guanya Shi
user
guanya
Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
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.
A learning-based control framework, Agile But Safe (ABS), enables quadrupedal robots to navigate cluttered environments safely and at high speeds by integrating agile and recovery policies guided by a reach-avoid value network.
agile policy recovery policy reach-avoid value network exteroception representation network quadrupedal robots high-speed navigation collision-free navigation