7 papers · 1 filter
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…
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…
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…
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…