5 papers · 1 filter
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…
Robust Agility via Learned Zero Dynamics Policies
Noel Csomay-Shanklin, William D. Compton, Ivan Dario Jimenez Rodriguez +3
We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a map…