1 citations · 1 across the 8 of their papers we have counts for
8 papers
KALIKO: Kalman-Implicit Koopman Operator Learning For Prediction of Nonlinear Dynamical Systems
Albert H. Li, Ivan Dario Jimenez Rodriguez, Joel W. Burdick +2
Long-horizon dynamical prediction is fundamental in robotics and control, underpinning canonical methods like model predictive control. Yet, many systems and disturbance phenomena…
Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis
Kejun Li, Jeeseop Kim, Maxime Brunet +3
Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predict…
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…
Practical Bayesian Algorithm Execution via Posterior Sampling
Chu Xin Cheng, Raul Astudillo, Thomas Desautels +1
We consider Bayesian algorithm execution (BAX), a framework for efficiently selecting evaluation points of an expensive function to infer a property of interest encoded as the outp…
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…
Constructive Nonlinear Control of Underactuated Systems via Zero Dynamics Policies
William Compton, Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin +2
Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the…
Preferential Multi-Objective Bayesian Optimization
Raul Astudillo, Kejun Li, Maegan Tucker +3
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple…
Data-Driven Predictive Control for Robust Exoskeleton Locomotion
Kejun Li, Jeeseop Kim, Xiaobin Xiong +3
Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive…