5 citations · 17 across the 14 of their papers we have counts for
18 papers
Reachability-Guided Sequential Quadratic Programming-Guarded Model Predictive Path Integral for Safe Nonlinear Predictive Control
Alexandre Didier, Jason J. Choi, Namhoon Cho +2
Safe robot control often requires combining long-horizon performance optimization with hard state and input constraints, but existing approaches tend to address this tradeoff parti…
Robust Direct Data-Driven Hamiltonian for Safe Set Computation under Measurement Noise and Disturbances
Mohammad Bajelani, Christopher A. Strong, Claire J. Tomlin +2
Safe set computation is a fundamental challenge in safety-critical control systems, especially in direct data-driven settings where safety analysis is performed directly from noise…
When are safety filters safe? On minimum phase conditions of control barrier functions
Jason J. Choi, Claire J. Tomlin, Shankar Sastry +1
In emerging control applications involving multiple and complex tasks, safety filters are gaining prominence as a modular approach to enforcing safety constraints. Among various me…
Whole-Body Bilateral Teleoperation with Multi-Stage Object Parameter Estimation for Wheeled Humanoid Locomanipulation
Donghoon Baek, Amartya Purushottam, Jason J. Choi +1
This paper presents an object-aware whole-body bilateral teleoperation framework for wheeled humanoid loco-manipulation. This framework combines whole-body bilateral teleoperation…
Resolving Conflicting Constraints in Multi-Agent Reinforcement Learning with Layered Safety
Jason J. Choi, Jasmine Jerry Aloor, Jingqi Li +3
Preventing collisions in multi-robot navigation is crucial for deployment. This requirement hinders the use of learning-based approaches, such as multi-agent reinforcement learning…
Data-Driven Hamiltonian for Direct Construction of Safe Set from Trajectory Data
Jason J. Choi, Christopher A. Strong, Koushil Sreenath +2
In continuous-time optimal control, evaluating the Hamiltonian requires solving a constrained optimization problem using the system's dynamics model. Hamilton-Jacobi reachability a…
Gait Switching and Enhanced Stabilization of Walking Robots with Deep Learning-based Reachability: A Case Study on Two-link Walker
Xingpeng Xia, Jason J. Choi, Ayush Agrawal +3
Learning-based approaches have recently shown notable success in legged locomotion. However, these approaches often lack accountability, necessitating empirical tests to determine…
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes
Will Lavanakul, Jason J. Choi, Koushil Sreenath +1
Learning-based approaches are emerging as an effective approach for safety filters for black-box dynamical systems. Existing methods have relied on certificate functions like Contr…
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…
A Forward Reachability Perspective on Control Barrier Functions and Discount Factors in Reachability Analysis
Jason J. Choi, Donggun Lee, Boyang Li +4
Control invariant sets are crucial for various methods that aim to design safe control policies for systems whose state constraints must be satisfied over an indefinite time horizo…
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 Density Models: Constraining Distribution Shift in Learning-Based Control
Katie Kang, Paula Gradu, Jason Choi +3
Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-di…
Computation of Regions of Attraction for Hybrid Limit Cycles Using Reachability: An Application to Walking Robots
Jason J. Choi, Ayush Agrawal, Koushil Sreenath +2
Contact-rich robotic systems, such as legged robots and manipulators, are often represented as hybrid systems. However, the stability analysis and region-of-attraction computation…
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…
Robust Control Barrier-Value Functions for Safety-Critical Control
Jason J. Choi, Donggun Lee, Koushil Sreenath +2
This paper works towards unifying two popular approaches in the safety control community: Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs). HJ Reachability ha…
Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability
Sylvia Herbert, Jason J. Choi, Suvansh Sanjeev +3
Autonomous systems like aircraft and assistive robots often operate in scenarios where guaranteeing safety is critical. Methods like Hamilton-Jacobi reachability can provide guaran…
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…
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…