63 citations · 107 across the 15 of their papers we have counts for
16 papers
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…
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…
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…
Learning Task Space Actions for Bipedal Locomotion
Helei Duan, Jeremy Dao, Kevin Green +3
Recent work has demonstrated the success of reinforcement learning (RL) for training bipedal locomotion policies for real robots. This prior work, however, has focused on learning…
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…
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…
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Kendall Lowrey, Svetoslav Kolev, Jeremy Dao +2
Reinforcement learning has emerged as a promising methodology for training robot controllers. However, most results have been limited to simulation due to the need for a large numb…