163 citations · 589 across the 42 of their papers we have counts for
10 papers · 1 filter
Dynamic Bipedal Maneuvers through Sim-to-Real Reinforcement Learning
Fangzhou Yu, Ryan Batke, Jeremy Dao +3
For legged robots to match the athletic capabilities of humans and animals, they must not only produce robust periodic walking and running, but also seamlessly switch between nomin…
Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning
Ryan Batke, Fangzhou Yu, Jeremy Dao +4
In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real r…
Learning Dynamic Bipedal Walking Across Stepping Stones
Helei Duan, Ashish Malik, Mohitvishnu S. Gadde +3
In this work, we propose a learning approach for 3D dynamic bipedal walking when footsteps are constrained to stepping stones. While recent work has shown progress on this problem,…
Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads
Jeremy Dao, Kevin Green, Helei Duan +2
Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of robustness and agility over a variety of terrains. However, that work, and most prior bipe…
Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking
Helei Duan, Ashish Malik, Jeremy Dao +5
Recently, work on reinforcement learning (RL) for bipedal robots has successfully learned controllers for a variety of dynamic gaits with robust sim-to-real demonstrations. In orde…
Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning
Jonah Siekmann, Kevin Green, John Warila +2
Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus, it is useful to have systems that do not depend on accurate es…