3 citations · 4 across the 13 of their papers we have counts for
17 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…
ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids
Gechen Qu, Tong Zhang, Bike Zhang +4
Safe control of humanoid robots remains challenging due to their high-dimensional dynamics, contact-rich interactions, and sensitivity to disturbances. Although reinforcement learn…
An Update to the Level Set Theorems in Hamilton-Jacobi Reachability Analysis
Dylan Hirsch, William McEneaney, Jaime Fisac +2
Hamilton-Jacobi Reachability (HJR) is an important framework for controlling safety-critical systems despite uncertainty. Its theoretical underpinnings are rooted in Hamilton-Jacob…
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…
Characterization and Analysis of Emergency Landing Flight Envelopes with Graded Safety Specifications
Chams Eddine Mballo, Bryce L. Ferguson, Inkyu Jang +2
Emergency landing flight envelope analysis traditionally adopts a binary notion of safety, whereby a trajectory is safe only if state constraints are satisfied pointwise in time. I…
Inverse Safety Filtering: Inferring Constraints from Safety Filters for Decentralized Coordination
Minh Nguyen, Jingqi Li, Gechen Qu +1
Safe multi-agent coordination in uncertain environments can benefit from learning constraints from other agents. Implicitly communicating safety constraints through actions is a pr…
From Global to Local: Hierarchical Probabilistic Verification for Reachability Learning
Ebonye Smith, Sampada Deglurkar, Jingqi Li +2
Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for nonlinear systems. However, it becomes computationally intractable in high-dimensional settings, motivating…
Active Calibration of Reachable Sets Using Approximate Pick-to-Learn
Sampada Deglurkar, Ebonye Smith, Jingqi Li +1
Reachability computations that rely on approximate or learned models require calibration in order to uphold confidence about their guarantees. Calibration generally involves sampli…
A Hamilton-Jacobi Reachability Framework with Soft Constraints for Safety-Critical Systems
Chams Eddine Mballo, Donggun Lee, Claire J. Tomlin
Traditional reachability methods provide formal guarantees of safety under bounded disturbances. However, they strictly enforce state constraints as inviolable, which can result in…
EigenSafe: A Spectral Framework for Learning-Based Probabilistic Safety Assessment
Inkyu Jang, Jonghae Park, Sihyun Cho +3
We present EigenSafe, an operator-theoretic framework for safety assessment of learning-enabled stochastic systems. In many robotic applications, the dynamics are inherently stocha…
Mechanistic interpretability for steering vision-language-action models
Bear Häon, Kaylene Stocking, Ian Chuang +1
Vision-Language-Action (VLA) models are a promising path to realizing generalist embodied agents that can quickly adapt to new tasks, modalities, and environments. However, methods…
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…
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…