Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems

TL;DR

Hyper-SAMARL is a hypergraph-based multi-robot system utilizing reinforcement learning to adaptively allocate tasks and navigate socially in dynamic human environments, improving efficiency and safety.

Contribution

The paper introduces Hyper-SAMARL, a novel hypergraph-based framework that models environmental interactions for adaptive task allocation and socially-aware navigation in multi-robot systems.

Findings

Outperforms baseline models in social navigation

Enhances task completion efficiency

Demonstrates high adaptability in dynamic scenarios

Abstract

A team of multiple robots seamlessly and safely working in human-filled public environments requires adaptive task allocation and socially-aware navigation that account for dynamic human behavior. Current approaches struggle with highly dynamic pedestrian movement and the need for flexible task allocation. We propose Hyper-SAMARL, a hypergraph-based system for multi-robot task allocation and socially-aware navigation, leveraging multi-agent reinforcement learning (MARL). Hyper-SAMARL models the environmental dynamics between robots, humans, and points of interest (POIs) using a hypergraph, enabling adaptive task assignment and socially-compliant navigation through a hypergraph diffusion mechanism. Our framework, trained with MARL, effectively captures interactions between robots and humans, adapting tasks based on real-time changes in human activity. Experimental results demonstrate that Hyper-SAMARL outperforms baseline models in terms of social navigation, task completion efficiency, and adaptability in various simulated scenarios.