title: CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
summary: We present CAJun, a novel hierarchical learning and control framework that
  enables legged robots to jump continuously with adaptive jumping distances.
  CAJun consists of a high-level centroidal policy and a low-level leg
  controller. In particular, we use reinforcement learning (RL) to train the
  centroidal policy, which specifies the gait timing, base velocity, and swing
  foot position for the leg controller. The leg controller optimizes motor
  commands for the swing and stance legs according to the gait timing to track
  the swing foot target and base velocity commands using optimal control.
  Additionally, we reformulate the stance leg optimizer in the leg controller to
  speed up policy training by an order of magnitude. Our system combines the
  versatility of learning with the robustness of optimal control. By combining RL
  with optimal control methods, our system achieves the versatility of learning
  while enjoys the robustness from control methods, making it easily transferable
  to real robots. We show that after 20 minutes of training on a single GPU,
  CAJun can achieve continuous, long jumps with adaptive distances on a Go1 robot
  with small sim-to-real gaps. Moreover, the robot can jump across gaps with a
  maximum width of 70cm, which is over 40% wider than existing methods.
paper_authors._id: 648fd9e906bb2b4ff27b573e
paper_authors.name: Yuxiang Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-20T08:50:01.874Z
paper_authors._id: 6490cd85ed034b1864d4f8e4
paper_authors.avatarUrl: /avatars/a8553246aae4492b042aa1260938dbe3.svg
paper_authors.fullname: Yuxiang Yang
paper_authors.type: user
paper_authors.user: yxyang
paper_authors._id: 648fd9e906bb2b4ff27b573f
paper_authors.name: Guanya Shi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-02T09:00:32.501Z
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_authors._id: 648fd9e906bb2b4ff27b5740
paper_authors.name: Xiangyun Meng
paper_authors._id: 648fd9e906bb2b4ff27b5741
paper_authors.name: Wenhao Yu
paper_authors._id: 648fd9e906bb2b4ff27b5742
paper_authors.name: Tingnan Zhang
paper_authors._id: 648fd9e906bb2b4ff27b5743
paper_authors.name: Jie Tan
paper_authors._id: 648fd9e906bb2b4ff27b5744
paper_authors.name: Byron Boots
paper_title: CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
paper_summary: We present CAJun, a novel hierarchical learning and control framework that
  enables legged robots to jump continuously with adaptive jumping distances.
  CAJun consists of a high-level centroidal policy and a low-level leg
  controller. In particular, we use reinforcement learning (RL) to train the
  centroidal policy, which specifies the gait timing, base velocity, and swing
  foot position for the leg controller. The leg controller optimizes motor
  commands for the swing and stance legs according to the gait timing to track
  the swing foot target and base velocity commands using optimal control.
  Additionally, we reformulate the stance leg optimizer in the leg controller to
  speed up policy training by an order of magnitude. Our system combines the
  versatility of learning with the robustness of optimal control. By combining RL
  with optimal control methods, our system achieves the versatility of learning
  while enjoys the robustness from control methods, making it easily transferable
  to real robots. We show that after 20 minutes of training on a single GPU,
  CAJun can achieve continuous, long jumps with adaptive distances on a Go1 robot
  with small sim-to-real gaps. Moreover, the robot can jump across gaps with a
  maximum width of 70cm, which is over 40% wider than existing methods.
paper_ai_summary: A novel hierarchical framework using reinforcement learning and optimal control enables a legged robot to perform continuous adaptive jumps with high versatility and robustness.
paper_ai_keywords: reinforcement learning centroidal policy leg controller optimal control motor commands sim-to-real transfer adaptive jumping distances