Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings

2025-07-03

AI Summary

Ensuring safety and social compliance in multi-robot coordination for human-centered social care scenarios remains challenging. Method: This paper proposes a multi-objective learning-based collaborative framework that jointly incorporates safety constraints and social behavioral norms. It is the first to integrate socially aware modeling—including crowd-sensitive navigation and human motion prediction—with hard safety constraints within a deep reinforcement learning architecture, coupled with distributed task allocation and dynamic scheduling to enable end-to-end joint optimization of path planning, task assignment, scheduling, and human–robot interaction. Results: Evaluations in both simulation and real-world care environments demonstrate significant improvements: +23.6% in task completion rate, +31.2% in social compliance score, −78.4% in collision rate, and −65.1% in interpersonal interference events. The framework establishes a scalable, socially aware coordination paradigm for multi-robot systems in assistive settings.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.

Problem

• Multi-robot coordination in multi-human social care settings
• Path planning and navigation in MHMR environments
• Task scheduling and allocation with human-robot interaction

Innovation

• Multi-objective learning-based coordination approach
• Path planning and navigation in MHMR
• Task scheduling and allocation strategy