4 papers
Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC
Devesh Nath, Anutam Srinivasan, Haoran Yin +3
We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models. Our approach trains an actio…
Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees
Devesh Nath, Haoran Yin, Glen Chou
We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN…
Formal Safety Verification and Refinement for Generative Motion Planners via Certified Local Stabilization
Devesh Nath, Haoran Yin, Glen Chou
We present a method for formal safety verification of learning-based generative motion planners. Generative motion planners (GMPs) offer advantages over traditional planners, but v…
Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration
David Molina Concha, Jiping Li, Haoran Yin +5
This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and c…