23 papers
MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
Manan Tayal, Aditya Singh, Shishir Kolathaya +1
Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-agent reinforcement learning (MARL), saf…
Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation
Hao Wang, Joshua Bowden, Colton Crosby +1
Policy evaluation is a fundamental component of the development and deployment pipeline for robotic policies. In modern manipulation systems, this problem is particularly challengi…
Neural Backward Reach-Avoid Tubes with MPC Supervision for High-Dimensional Systems: An Application to Safe Spacecraft Docking
Santiago Thorup, Luca Castelletto, Zeyuan Feng +1
Autonomous spacecraft docking requires control policies that simultaneously ensure collision avoidance and target reachability under coupled, high-dimensional translational-rotatio…
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…
Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis
Albert Lin, Alessandro Pinto, Somil Bansal
As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally verify their safety and performance despi…