NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning

TL;DR

This paper introduces Neural Hamilton-Jacobi Reachability Learning (HJR), a scalable and data-efficient decentralized approach for safe multi-agent motion planning that handles high-dimensional spaces and complex collision constraints in real-time.

Contribution

It presents a novel neural HJR modeling framework combined with decentralized trajectory optimization for scalable, real-time multi-agent motion planning.

Findings

Outperforms state-of-the-art methods in complex scenarios

Handles high-dimensional configuration spaces effectively

Demonstrates generalization across various dynamical systems

Abstract

Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. To address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi-Agent Motion Planning. Our method provides scalable neural HJR modeling to tackle high-dimensional configuration spaces and capture worst-case collision and safety constraints between agents. We further propose a decentralized trajectory optimization framework that incorporates the learned HJR solutions to solve MAMP tasks in real-time. We demonstrate that our method is both scalable and data-efficient, enabling the solution of MAMP problems in higher-dimensional scenarios with complex collision constraints. Our approach generalizes across various dynamical systems, including a 12-dimensional dual-arm setup, and outperforms a range of state-of-the-art techniques in successfully addressing challenging MAMP tasks. Video demonstrations are available at https://youtu.be/IZiePX0p1Mc.