1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Hao Wang, Nam Nguyen, Armand Jordana +2
Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state and input constraints. Althou…
Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks
Hao Wang, Sathwik Karnik, Bea Lim +1
Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often lea…
Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis
Hao Wang, Armand Jordana, Ludovic Righetti +1
While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and sa…
Providing Safety Assurances for Systems with Unknown Dynamics
Hao Wang, Javier Borquez, Somil Bansal
As autonomous systems become more complex and integral in our society, the need to accurately model and safely control these systems has increased significantly. In the past decade…
On Safety and Liveness Filtering Using Hamilton-Jacobi Reachability Analysis
Javier Borquez, Kaustav Chakraborty, Hao Wang +1
Hamilton-Jacobi (HJ) reachability-based filtering provides a powerful framework to co-optimize performance and safety (or liveness) for autonomous systems. Under this filtering sch…