UrbanHuRo: A Two-Layer Human-Robot Collaboration Framework for the Joint Optimization of Heterogeneous Urban Services

2026-03-03

AI Summary

This work addresses the prevalent limitation in urban service optimization—namely, the isolated treatment of heterogeneous tasks that neglects their synergistic potential. To bridge this gap, we propose UrbanHuRo, a novel two-tier human-robot collaboration framework that jointly optimizes heterogeneous urban services such as crowdsourced delivery and city-scale sensing. The upper tier employs a MapReduce-based distributed K-submodular maximization module for order dispatching, while the lower tier leverages a deep submodular reward reinforcement learning algorithm to plan sensing trajectories, effectively balancing conflicting objectives and enabling real-time coordination. Experiments on real-world data from a food-delivery platform demonstrate that our approach improves sensing coverage by 29.7% on average, increases rider income by 39.2%, and significantly reduces the rate of late deliveries.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In the vision of smart cities, technologies are being developed to enhance the efficiency of urban services and improve residents' quality of life. However, most existing research focuses on optimizing individual services in isolation, without adequately considering reciprocal interactions among heterogeneous urban services that could yield higher efficiency and improved resource utilization. For example, human couriers could collect traffic and air quality data along their delivery routes, while sensing robots could assist with on-demand delivery during peak hours, enhancing both sensing coverage and delivery efficiency. However, the joint optimization of different urban services is challenging due to potentially conflicting objectives and the need for real-time coordination in dynamic environments. In this paper, we propose UrbanHuRo, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing. UrbanHuRo includes two key designs: (i) a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch, and (ii) a deep submodular reward reinforcement learning algorithm for sensing route planning. Experimental evaluations on real-world datasets from a food delivery platform demonstrate that UrbanHuRo improves sensing coverage by 29.7% and courier income by 39.2% on average in most settings, while also significantly reducing the number of overdue orders.

Problem

• urban services
• heterogeneous services
• joint optimization
• human-robot collaboration
• smart cities

Innovation

• human-robot collaboration
• joint optimization
• submodular maximization
• reinforcement learning
• urban services