6 papers
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…
Stability of Control Lyapunov Function Guided Reinforcement Learning
Zachary Olkin, William D. Compton, Aaron D. Ames
Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corresponding control policies is…
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…
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…
Compatibility of Multiple Control Barrier Functions for Constrained Nonlinear Systems
Max H. Cohen, Eugene Lavretsky, Aaron D. Ames
Control barrier functions (CBFs) are a powerful tool for the constrained control of nonlinear systems; however, the majority of results in the literature focus on systems subject t…
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…