33 citations · 37 across the 7 of their papers we have counts for
7 papers · 1 filter
Simulator Adaptation for Sim-to-Real Learning of Legged Locomotion via Proprioceptive Distribution Matching
Jeremy Dao, Alan Fern
Simulation trained legged locomotion policies often exhibit performance loss on hardware due to dynamics discrepancies between the simulator and the real world, highlighting the ne…
Optimizing Bipedal Locomotion for The 100m Dash With Comparison to Human Running
Devin Crowley, Jeremy Dao, Helei Duan +3
In this paper, we explore the space of running gaits for the bipedal robot Cassie. Our first contribution is to present an approach for optimizing gait efficiency across a spectrum…
No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain
Mohitvishnu S. Gadde, Pranay Dugar, Ashish Malik +1
Effective bipedal locomotion in dynamic environments, such as cluttered indoor spaces or uneven terrain, requires agile and adaptive movement in all directions. This necessitates o…
Learning Decentralized Multi-Biped Control for Payload Transport
Bikram Pandit, Ashutosh Gupta, Mohitvishnu S. Gadde +5
Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectivene…
Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walking
Bart van Marum, Aayam Shrestha, Helei Duan +3
A necessary capability for humanoid robots is the ability to stand and walk while rejecting natural disturbances. Recent progress has been made using sim-to-real reinforcement lear…
Sim-to-Real Learning for Humanoid Box Loco-Manipulation
Jeremy Dao, Helei Duan, Alan Fern
In this work we propose a learning-based approach to box loco-manipulation for a humanoid robot. This is a particularly challenging problem due to the need for whole-body coordinat…
Learning Vision-Based Bipedal Locomotion for Challenging Terrain
Helei Duan, Bikram Pandit, Mohitvishnu S. Gadde +4
Reinforcement learning (RL) for bipedal locomotion has recently demonstrated robust gaits over moderate terrains using only proprioceptive sensing. However, such blind controllers…