Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints

2026-01-05

arXiv.org

AI Summary

This work addresses the challenge of jointly optimizing task assignment, scheduling, and path planning in multi-robot systems under spatiotemporal and resource constraints to maximize mission efficacy. The authors propose the STEAM problem framework, which—unlike prior approaches—models task efficacy as a continuous function and introduces a trait-efficacy mapping that captures the relationship between robot capabilities and task performance. To efficiently learn this mapping, they design a feasibility-aware active learning mechanism and develop E-ITAGS, an incremental task assignment graph search algorithm that simultaneously optimizes task efficacy and temporal constraints. Experimental results in emergency response scenarios demonstrate that E-ITAGS significantly improves task efficacy while strictly satisfying all constraints; furthermore, the active learning component exhibits high sample efficiency and comes with theoretical guarantees on suboptimality.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Efficacy-optimized Allocation for Multi-robot systems (STEAM). STEAM builds upon trait-based frameworks that model robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success-failure model by explicitly modeling the efficacy of allocations as trait-efficacy maps. These maps encode how the aggregated capabilities assigned to a task determine performance. Further, STEAM accommodates spatio-temporal constraints, including a user-specified time budget (i.e., maximum makespan). To solve STEAM problems, we contribute a novel algorithm named Efficacy-optimized Incremental Task Allocation Graph Search (E-ITAGS) that simultaneously optimizes task performance and respects time budgets by interleaving task allocation, scheduling, and path planning. Motivated by the fact that trait-efficacy maps are difficult, if not impossible, to specify, E-ITAGS efficiently learns them using a realizability-aware active learning module. Our approach is realizability-aware since it explicitly accounts for the fact that not all combinations of traits are realizable by the robots available during learning. Further, we derive experimentally-validated bounds on E-ITAGS'suboptimality with respect to efficacy. Detailed numerical simulations and experiments using an emergency response domain demonstrate that E-ITAGS generates allocations of higher efficacy compared to baselines, while respecting resource and spatio-temporal constraints. We also show that our active learning approach is sample efficient and establishes a principled tradeoff between data and computational efficiency.

Problem

• multi-robot task allocation
• spatio-temporal constraints
• trait-efficacy modeling
• time budget
• resource constraints

Innovation

• trait-efficacy maps
• spatio-temporal constraints
• active learning
• multi-robot task allocation
• E-ITAGS