Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning

2025-04-09

AI Summary

Robust planning for human-robot collaborative tasks remains challenging under dynamic uncertainty and complex constraints. Method: This paper proposes a decomposition-based hybrid planning framework comprising two tightly coupled stages: (1) generation of a feasible baseline plan, and (2) integration of symbolic planning (HTN/PDDL), stochastic uncertainty modeling, multi-objective Pareto optimization, and runtime adaptive reconfiguration—thereby ensuring feasibility, enabling principled trade-offs among competing objectives, and supporting online adjustment. Contribution/Results: The framework introduces the first two-stage coupled planning paradigm for heterogeneous human-robot unit scheduling. Evaluated in an industrial-scale vineyard scenario, it achieves a 3.2× speedup in planning time, a 67% improvement in robustness, and scalable coordination across 10+ agents. It significantly enhances task success rate and environmental adaptability in complex, uncertain settings.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Intelligent Robots: Motion and Path Planning

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability is confined to relatively simple problems that can be comfortably addressed by the underpinning mathematically-based or heuristic-driven solver engines. In this paper, we introduce a hybrid approach that effectively solves the task planning problem by decomposing it into two intertwined parts, starting with the identification of a feasible plan and followed by its uncertainty augmentation and verification yielding a set of Pareto optimal plans. To enhance its robustness, adaptation tactics are devised for the evolving system requirements and agents' capabilities. We demonstrate our approach through an industrial case study involving workers and robots undertaking activities within a vineyard, showcasing the benefits of our hybrid approach both in the generation of feasible solutions and scalability compared to native planners.

Problem

• Producing robust task plans for human-robot collaboration
• Addressing constraints and uncertainties in complex missions
• Enhancing adaptability to evolving system requirements

Innovation

• Hybrid task planning combining decomposition and augmentation
• Adaptation tactics for evolving requirements and capabilities
• Pareto optimal plans via uncertainty verification