activity
20212023
most citedSafety-Aware Preference-Based Learning for Safety-Critical Control

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2023

Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics

Adrian B. Ghansah, Jeeseop Kim, Maegan Tucker +1

Selecting robot design parameters can be challenging since these parameters are often coupled with the performance of the controller and, therefore, the resulting capabilities of t…

eess.SY2023

Input-to-State Stability in Probability

Preston Culbertson, Ryan K. Cosner, Maegan Tucker +1

Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances. If a system is ISS, its trajectories wil…

cs.RO20231 cited

An Input-to-State Stability Perspective on Robust Locomotion

Maegan Tucker, Aaron D. Ames

Uneven terrain necessarily transforms periodic walking into a non-periodic motion. As such, traditional stability analysis tools no longer adequately capture the ability of a biped…

cs.RO20221 cited

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…

cs.RO20215 cited

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…