MORPH: Self-Organising Multi-Robot Task Allocation via Neuroplasticity-Inspired Adaptive Topology

2026-09-26

AI Summary

This study addresses the limited adaptability of dynamic multi-robot task allocation methods that rely on prior knowledge or offline training. We propose a training-free online learning framework that pioneers the online acquisition of directed preferences during instantaneous single-task assignment. By leveraging synaptic plasticity, homeostatic mechanisms, and runtime co-occurrence feedback, the framework enables adaptive topology updates that drive the emergence of globally coordinated behaviors without requiring cost matrices or offline training phases. Experimental evaluations on warehouse benchmarks demonstrate that the proposed method achieves 110% of the throughput attained by a fully connected baseline while utilizing only 21% of the coordination links, significantly outperforming conventional proximity-based approaches.

Abstract

Multi-robot task allocation (MRTA) in dynamic environments faces a fundamental tension: effective coordination requires learned structure, but that structure must adapt when conditions change. Existing methods resolve this by assuming prior task knowledge, a utility function, a cost matrix, or a trained policy making them brittle when deployed without such knowledge or when task distributions shift. We present MORPH(Multi-agent Online Rewiring through Plasticity-guided Hierarchy), a training-free MRTA framework where global allocation quality emerges from 4 local plasticity rules (synaptic, homeostatic, structural, and metaplasticity) applied to a directed pairwise preference matrix updated from runtime co-occurrence and task-completion feedback. MORPH requires no task model, no bid computation, and no offline training; response decisions use learned AGV-to-Picker preferences rather than a fixed proximity rule. Within the Gerkey-Mataric MRTA taxonomy, MORPH is the first method in the single-task, single-robot, instantaneous-assignment class to learn directed pairwise allocation preferences online. Evaluated on the TA-RWARE warehouse benchmark (8-24 agents, 4 maps, 800 steps per episode, 5 seeds), MORPH achieves 110% of all-to-all throughput at N=24 while using only 21% of possible coordination links as an efficiency advantage that grows monotonically with fleet size. Under spatial task distribution shift, MORPH degrades 3x less than proximity-based methods while its learned preferences remain uncorrelated with Manhattan distance. Systematic ablation confirms all four plasticity rules contribute measurably. Two allocation properties emerge without programming: cross-type preference dominance and progressive preference sparsification, mirroring the developmental refinement of biological neural circuits. Learned preferences are driven by task co-occurrence history, not spatial proximity.

Problem

• Multi-robot task allocation
• Dynamic environments
• Task distribution shift
• Adaptive coordination

Innovation

• Multi-Robot Task Allocation
• Neuroplasticity
• Training-Free
• Self-Organising Topology
• Online Preference Learning