1 citations · 1 across the 3 of their papers we have counts for
5 papers
No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Pranay Dugar, Mohitvishnu S. Gadde, Jonah Siekmann +3
Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical, these transitions must be fast, robu…
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…
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 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…