4 papers · 1 filter
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…
CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
Kejun Li, Zachary Olkin, Yisong Yue +1
Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly sh…
Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control
Zachary Olkin, Aaron D. Ames
Computing stabilizing and optimal control actions for legged locomotion in real time is difficult due to the nonlinear, hybrid, and high dimensional nature of these robots. The hyb…
Layered Nonlinear Model Predictive Control for Robust Stabilization of Hybrid Systems
Zachary Olkin, Aaron D. Ames
Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation. To address this challenge, we propose…