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Found 4 papers, 2 papers with code
Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads
We show that prior RL policies trained for unloaded locomotion fail for some loads and that simply training in the context of loads is enough to result in successful and improved policies.
Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL).
Robotics
Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie
Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills.
Robotics
Feedback Control For Cassie With Deep Reinforcement Learning
By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL.
Robotics
