63 citations · 64 across the 3 of their papers we have counts for
6 papers
Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
Jonah Siekmann, Yesh Godse, Alan Fern +1
We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL). A key challenge of learning legged locomotio…
Learning Spring Mass Locomotion: Guiding Policies with a Reduced-Order Model
Kevin Green, Yesh Godse, Jeremy Dao +3
In this paper, we describe an approach to achieve dynamic legged locomotion on physical robots which combines existing methods for control with reinforcement learning. Specifically…
Learning Memory-Based Control for Human-Scale Bipedal Locomotion
Jonah Siekmann, Srikar Valluri, Jeremy Dao +4
Controlling a non-statically stable biped is a difficult problem largely due to the complex hybrid dynamics involved. Recent work has demonstrated the effectiveness of reinforcemen…
Planning for the Unexpected: Explicitly Optimizing Motions for Ground Uncertainty in Running
Kevin Green, Ross L. Hatton, Jonathan Hurst
We propose a method to generate actuation plans for a reduced order, dynamic model of bipedal running. This method explicitly enforces robustness to ground uncertainty. The plan ge…
Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie
Zhaoming Xie, Patrick Clary, Jeremy Dao +3
Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills. However, the iterative design process that is inevitable in practice is poorly su…
Feedback Control For Cassie With Deep Reinforcement Learning
Zhaoming Xie, Glen Berseth, Patrick Clary +2
Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tract…