6 papers · 1 filter
Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
William D. Compton, Zachary Olkin, Aaron D. Ames
We present a method for training reference-guided, perceptive reinforcement learning locomotion policies for humanoid robots in which reference trajectories are modulated in traini…
Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running
Zachary Olkin, William D. Compton, Ryan M. Bena +1
Humanoid robots have the promise of locomoting like humans, including fast and dynamic running. Recently, reinforcement learning (RL) controllers that can mimic human motions have…
Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds
Min Dai, William D. Compton, Junheng Li +2
Bipedal humanoid robots must precisely coordinate balance, timing, and contact decisions when locomoting on constrained footholds such as stepping stones, beams, and planks -- even…
Chasing Stability: Humanoid Running via Control Lyapunov Function Guided Reinforcement Learning
Zachary Olkin, Kejun Li, William D. Compton +1
Achieving highly dynamic behaviors on humanoid robots, such as running, requires controllers that are both robust and precise, and hence difficult to design. Classical control meth…
Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice
William D. Compton, Noel Csomay-Shanklin, Cole Johnson +1
Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed o…
Dynamically Feasible Path Planning in Cluttered Environments via Reachable Bezier Polytopes
Noel Csomay-Shanklin, William D. Compton, Aaron D. Ames
The deployment of robotic systems in real world environments requires the ability to quickly produce paths through cluttered, non-convex spaces. These planned trajectories must be…