Learning Stable and Energetically Economical Walking with RAMone
This work addresses energy-efficient locomotion for bipedal robots, but it appears incremental as it applies existing methods to a specific robot.
The paper tackled the problem of optimizing control parameters for stable and energy-efficient walking in a planar-bipedal robot, RAMone, across various speeds, using episodic reinforcement learning with Covariance Matrix Adaptation.
In this paper, we optimize over the control parameter space of our planar-bipedal robot, RAMone, for stable and energetically economical walking at various speeds. We formulate this task as an episodic reinforcement learning problem and use Covariance Matrix Adaptation. The parameters we are interested in modifying include gains from our Hybrid Zero Dynamics style controller and from RAMone's low-level motor controllers.