title: Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
summary: 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.
paper_authors._id: 65bb0c77e3ffc2ba202979df
paper_authors.name: Tairan He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-02T09:01:26.014Z
paper_authors._id: 65bc04ca719492167d710847
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uW8Cqff2XyFYJCKWHlgO4.png
paper_authors.fullname: Tairan He
paper_authors.type: user
paper_authors.user: tairanhe
paper_authors._id: 65bb0c77e3ffc2ba202979e0
paper_authors.name: Chong Zhang
paper_authors._id: 65bb0c77e3ffc2ba202979e1
paper_authors.name: Wenli Xiao
paper_authors._id: 65bb0c77e3ffc2ba202979e2
paper_authors.name: Guanqi He
paper_authors._id: 65bb0c77e3ffc2ba202979e3
paper_authors.name: Changliu Liu
paper_authors._id: 65bb0c77e3ffc2ba202979e4
paper_authors.name: Guanya Shi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-02T09:01:28.063Z
paper_authors._id: 65bbcbadadc958a7d9ef8914
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/9Pv8b1nUXIP2fvTS281DK.jpeg
paper_authors.fullname: Guanya Shi
paper_authors.type: user
paper_authors.user: guanya
paper_title: Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: agile policy recovery policy reach-avoid value network exteroception representation network quadrupedal robots high-speed navigation collision-free navigation