From the 1 of 9 linked papers with an AI index.
9 papers
An Update to the Level Set Theorems in Hamilton-Jacobi Reachability Analysis
Dylan Hirsch, William McEneaney, Jaime Fisac +2
Hamilton-Jacobi Reachability (HJR) is an important framework for controlling safety-critical systems despite uncertainty. Its theoretical underpinnings are rooted in Hamilton-Jacob…
Exact Decomposition of Adversarial Dual-Objective Value Functions, with Applications to Optimal Drug Dosing
Dylan Hirsch, William Sharpless, Sylvia Herbert
The paper proves that certain decompositions of dual‑objective value functions remain valid when an adversary is present, and demonstrates how this can be used to design optimal dr…
Bellman Value Decomposition for Task Logic in Safe Optimal Control
William Sharpless, Oswin So, Dylan Hirsch +2
Real-world tasks involve nuanced combinations of goal and safety specifications. In high dimensions, the challenge is exacerbated: formal automata become cumbersome, and the combin…
Safe Stochastic Explorer: Enabling Safe Goal Driven Exploration in Stochastic Environments and Safe Interaction with Unknown Objects
Nikhil Uday Shinde, Dylan Hirsch, Michael C. Yip +1
Autonomous robots operating in unstructured, safety-critical environments, from planetary exploration to warehouses and homes, must learn to safely navigate and interact with their…
Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter
Haixin Jin, Nikhil Uday Shinde, Soofiyan Atar +5
Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional…
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations
William Sharpless, Dylan Hirsch, Sander Tonkens +2
Hard constraints in reinforcement learning (RL) often degrade policy performance. Lagrangian methods offer a way to blend objectives with constraints, but require intricate reward…