3 papers
cs.LG2025
Meta-Learning for Physically-Constrained Neural System Identification
Ankush Chakrabarty, Gordon Wichern, Vedang M. Deshpande +3
We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorpor…
math.OC2024
Projection-free computation of robust controllable sets with constrained zonotopes
Abraham P. Vinod, Avishai Weiss, Stefano Di Cairano
We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and ou…
cs.RO2023
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty +3
We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon…