BOTH: Efficient Coordination of Mobile Agents With Graph-Enhanced Bayesian Online Learning

Collaborative agents, consisting of at least one human and one mobile robot agent working toward a common objective, are increasingly prevalent and effective in both social and industrial spheres, such as manufacturing. The inherent heterogeneity of these agents requires efficient and scalable Task Scheduling and Allocation (TSA) schemes that match individuals to tasks based on their abilities and meet specific temporal constraints, maximizing performance in less time. Existing works face challenges as exact methods rely on assumptions and deterministic models, which struggle to scale and infer time-varying, stochastic human task performance. While offline reinforcement learning shows promise, it is time-consuming and heavily dependent on training data that is often scarce in practical factory settings. To address these challenges, we formulate the TSA problem in mobile multi-agent teams as a temporal-constrained contextual decision-making process and propose the Bayesian Optimization-augmented Team coordination among Heterogeneous agents (BOTH), a novel scalable and training-free scheduling approach. The core idea is to use Gaussian Processes (GP) to iteratively infer agent dynamics in real-time, enabling the automatic derivation of a robust TSA solution that requires no prior data and adapts to varying problem sizes. We start by employing a heterogeneous graph-based encoder to extract representative context from the individual differences among team agents and tasks, considering strict temporal constraints. Following this, we propose a GP-driven Bayesian optimizer to intelligently explore and exploit optimal task assignments for each context, without making assumptions about the system. Experiments on synthetic and real datasets demonstrate that BOTH boosts accuracy and time efficiency compared to competing baselines, even within a few iterations.