7 citations · 23 across the 7 of their papers we have counts for
9 papers
Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems
Jason J. Choi, Fernando Castañeda, Wonsuhk Jung +3
As the use of autonomous robots expands in tasks that are complex and challenging to model, the demand for robust data-driven control methods that can certify safety and stability…
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States
Fernando Castañeda, Haruki Nishimura, Rowan McAllister +2
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems. Nonetheless, the lea…
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
Fernando Castañeda, Jason J. Choi, Wonsuhk Jung +3
Learning-based control has recently shown great efficacy in performing complex tasks for various applications. However, to deploy it in real systems, it is of vital importance to g…
Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning
Tyler Westenbroek, Fernando Castaneda, Ayush Agrawal +2
Recent advances in the reinforcement learning (RL) literature have enabled roboticists to automatically train complex policies in simulated environments. However, due to the poor s…
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…
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…
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…
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…
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…