6 citations · 9 across the 5 of their papers we have counts for
6 papers
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue +1
This work presents Neural Gaits, a method for learning dynamic walking gaits through the enforcement of set invariance that can be refined episodically using experimental data from…
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Noel Csomay-Shanklin, Andrew J. Taylor, Ugo Rosolia +1
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with di…
Interactive multi-modal motion planning with Branch Model Predictive Control
Yuxiao Chen, Ugo Rosolia, Wyatt Ubellacker +2
Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be con…
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai +2
This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only vali…
Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots
Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma +1
This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning.…
Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion
Wen-Loong Ma, Noel Csomay-Shanklin, Aaron D. Ames
A robotic system can be viewed as a collection of lower-dimensional systems that are coupled via reaction forces (Lagrange multipliers) enforcing holonomic constraints. Inspired by…