most citedImproving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning

6 citations · 6 across the 1 of their papers we have counts for

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5 papers

eess.SY2021

Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty

Fernando Castañeda, Jason J. Choi, Bike Zhang +2

Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. They are commonly…

eess.SY2020

Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics

Fernando Castañeda, Jason J. Choi, Bike Zhang +2

This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Pr…

eess.SY20206 cited

Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning

Fernando Castañeda, Mathias Wulfman, Ayush Agrawal +4

The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. Model uncertainty is common…

math.OC2020

Learning Min-norm Stabilizing Control Laws for Systems with Unknown Dynamics

Tyler Westenbroek, Fernando Castaneda, Ayush Agrawal +2

This paper introduces a framework for learning a minimum-norm stabilizing controller for a system with unknown dynamics using model-free policy optimization methods. The approach b…

eess.SY2020

Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions

Jason Choi, Fernando Castañeda, Claire J. Tomlin +1

In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-ouput line…