Fast and Efficient Locomotion via Learned Gait Transitions
- URL: http://arxiv.org/abs/2104.04644v1
- Date: Fri, 9 Apr 2021 23:53:28 GMT
- Title: Fast and Efficient Locomotion via Learned Gait Transitions
- Authors: Yuxiang Yang, Tingnan Zhang, Erwin Coumans, Jie Tan, Byron Boots
- Abstract summary: We focus on the problem of developing efficient controllers for quadrupedal robots.
We devise a hierarchical learning framework, in which distinctive locomotion gaits and natural gait transitions emerge automatically.
We show that the learned hierarchical controller consumes much less energy across a wide range of locomotion speed than baseline controllers.
- Score: 35.86279693549959
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We focus on the problem of developing efficient controllers for quadrupedal
robots. Animals can actively switch gaits at different speeds to lower their
energy consumption. In this paper, we devise a hierarchical learning framework,
in which distinctive locomotion gaits and natural gait transitions emerge
automatically with a simple reward of energy minimization. We use reinforcement
learning to train a high-level gait policy that specifies the contact schedules
of each foot, while the low-level Model Predictive Controller (MPC) optimizes
the motor torques so that the robot can walk at a desired velocity using that
gait pattern. We test our learning framework on a quadruped robot and
demonstrate automatic gait transitions, from walking to trotting and to
fly-trotting, as the robot increases its speed up to 2.5m/s (5 body lengths/s).
We show that the learned hierarchical controller consumes much less energy
across a wide range of locomotion speed than baseline controllers.
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