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

TL;DR

This paper introduces STEAM, a new problem class for multi-robot task allocation that models efficacy explicitly and accounts for spatio-temporal constraints, and proposes E-ITAGS, an algorithm that learns efficacy maps and optimizes task performance.

Contribution

The paper formalizes STEAM, a novel multi-robot task allocation problem with efficacy modeling and spatio-temporal constraints, and develops E-ITAGS, a learning and optimization algorithm for it.

Findings

E-ITAGS outperforms baselines in efficacy and constraint satisfaction.

Active learning efficiently learns trait-efficacy maps.

Bounds on suboptimality are experimentally validated.

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.