5 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
Dynamic Walking on Highly Underactuated Point Foot Humanoids: Closing the Loop between HZD and HLIP
Adrian B. Ghansah, Jeeseop Kim, Kejun Li +1
Realizing bipedal locomotion on humanoid robots with point feet is especially challenging due to their highly underactuated nature, high degrees of freedom, and hybrid dynamics res…
POLAR: Preference Optimization and Learning Algorithms for Robotics
Maegan Tucker, Kejun Li, Yisong Yue +1
Parameter tuning for robotic systems is a time-consuming and challenging task that often relies on domain expertise of the human operator. Moreover, existing learning methods are n…
Safety-Aware Preference-Based Learning for Safety-Critical Control
Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor +7
Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conser…