CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
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.
648fd9e906bb2b4ff27b573e
Yuxiang Yang
claimed_verified
2023-06-20T08:50:01.874Z
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/avatars/a8553246aae4492b042aa1260938dbe3.svg
Yuxiang Yang
user
yxyang
648fd9e906bb2b4ff27b573f
Guanya Shi
claimed_verified
2024-02-02T09:00:32.501Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/9Pv8b1nUXIP2fvTS281DK.jpeg
Guanya Shi
user
guanya
648fd9e906bb2b4ff27b5740
Xiangyun Meng
648fd9e906bb2b4ff27b5741
Wenhao Yu
648fd9e906bb2b4ff27b5742
Tingnan Zhang
648fd9e906bb2b4ff27b5743
Jie Tan
648fd9e906bb2b4ff27b5744
Byron Boots
CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
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.
A novel hierarchical framework using reinforcement learning and optimal control enables a legged robot to perform continuous adaptive jumps with high versatility and robustness.
reinforcement learning centroidal policy leg controller optimal control motor commands sim-to-real transfer adaptive jumping distances