13 papers
Learning Keypoint Constraints from Visual Demonstrations for Safe Motion Planning
Zheng Qiu, Zhouyu Zhang, Ninghan Zhong +1
We present Keypoint Constraint Learning (KCL), a framework for learning task-relevant motion constraints from visual demonstrations and for planning safely with the extracted const…
Effective nonvanishing of -trivial regular surfaces over imperfect fields
Jingshan Chen, Chongning Wang, Lei Zhang
For any regular projective surface over an arbitrary field of characteristic with , we prove that \[ 60K_X\sim0 . \] This uniform bound is optimal. To sho…
Safe, Real-Time Active Model Discrimination and Fault Diagnosis for Nonlinear Systems via Differentiable Reachability
Xinpei Ni, Melkior Ornik, Glen Chou +1
We present a safe, real-time algorithm for active fault diagnosis and model discrimination for uncertain continuous-time nonlinear systems with process and measurement disturbances…
PolyMerge: Compressing 3D Gaussian Splats with Polytope Coverings for Provably Safe Resource-Constrained Navigation
Jihoon Hong, Chih-Yuan Chiu, Sara Fridovich-Keil +1
Obstacle avoidance is essential for safe navigation and motion planning. Recent radiance field reconstruction methods enable object detection and modeling with high fidelity, but r…
Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications
Liqian Ma, Zishun Liu, Glen Chou +1
We study feedback motion planning for continuous-time stochastic nonlinear systems under signal temporal logic (STL) specifications. We propose a framework that synthesizes control…
Over-Approximating Minimizer Sets of Constrained Convex Programs with Parametric Uncertainty via Reachability Analysis
Brendan Gould, Chih-Yuan Chiu, Antoine P. Leeman +3
We study the set of solutions to a parameterized, strongly convex optimization problem whose cost depends on uncertain, bounded parameters. We compute a certified outer approximati…
VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis
Antoine P. Leeman, Shuyu Zhan, Melanie N. Zeilinger +1
We propose VISION-SLS, a method for nonlinear output-feedback control from high-resolution RGB images which provides robust constraint satisfaction guarantees under calibrated unce…
Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Saurabh Bagchi, Hyunseung Kim, Tarek Abdelzaher +20
Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks…
Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
Jeffrey Fang, Glen Chou
We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain robotic systems and long planni…
Active Constraint Learning in High Dimensions from Demonstrations
Zheng Qiu, Chih-Yuan Chiu, Glen Chou
We present an iterative active constraint learning (ACL) algorithm, within the learning from demonstrations (LfD) paradigm, which intelligently solicits informative demonstration t…
Robustly Constrained Dynamic Games for Uncertain Nonlinear Dynamics
Shuyu Zhan, Chih-Yuan Chiu, Antoine P. Leeman +1
We propose a novel framework for robust dynamic games with nonlinear dynamics corrupted by state-dependent additive noise, and nonlinear agent-specific and shared constraints. Leve…
Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations
Chih-Yuan Chiu, Zhouyu Zhang, Glen Chou
We present a method for learning unknown parametric constraints from locally-optimal input-output trajectory data. We assume the data is generated by rollouts of stochastic nonline…
Constraint Learning in Multi-Agent Dynamic Games from Demonstrations of Local Nash Interactions
Zhouyu Zhang, Chih-Yuan Chiu, Glen Chou
We present an inverse dynamic game-based algorithm to learn parametric constraints from a given dataset of local Nash equilibrium interactions between multiple agents. Specifically…