Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks

TL;DR

This paper introduces a scalable reinforcement learning framework for multi-robot task allocation in multi-object transportation, effectively handling infeasible tasks and preventing deadlocks through experience-based exclusion and dynamic prioritization.

Contribution

It presents a novel, scalable RL-based task allocation method that enables robots to exclude infeasible tasks and adapt to varying robot and object numbers without prior feasibility knowledge.

Findings

The method successfully prevents deadlocks in multi-robot transport scenarios.

Scalability confirmed with increased robots and objects, including unlearned weights.

Temporary deadlock avoidance improves system robustness.

Abstract

Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable cooperative transport. However, allocating transportation tasks of objects with unknown weights remains challenging. Moreover, the presence of infeasible tasks (untransportable objects) can lead to robot stoppage (deadlock). This paper proposes a framework for dynamic task allocation that involves storing task experiences for each task in a scalable manner with respect to the number of robots. First, these experiences are broadcasted from the cloud server to the entire robot system. Subsequently, each robot learns the exclusion levels for each task based on those task experiences, enabling it to exclude infeasible tasks and reset its task priorities. Finally, individual transportation, cooperative transportation, and the temporary exclusion of tasks considered infeasible are achieved. The scalability and versatility of the proposed method were confirmed through numerical experiments with an increased number of robots and objects, including unlearned weight objects. The effectiveness of the temporary deadlock avoidance was also confirmed by introducing additional robots within an episode. The proposed method enables the implementation of task allocation strategies that are feasible for different numbers of robots and various transport tasks without prior consideration of feasibility.