Heterogeneous Embodied Multi-Agent Collaboration

TL;DR

This paper introduces a new benchmark and hierarchical decision model for heterogeneous multi-agent collaboration in indoor tidying-up tasks, leveraging diverse agent capabilities for efficient object organization.

Contribution

It presents the first heterogeneous multi-agent tidying-up benchmark dataset and a hierarchical decision model with handshake-based communication for complex indoor tasks.

Findings

The proposed model effectively detects misplaced objects and predicts suitable receptacles.

Heterogeneous agents outperform homogeneous counterparts in tidying-up tasks.

Extensive experiments validate the model's efficiency and practicality.

Abstract

Multi-agent embodied tasks have recently been studied in complex indoor visual environments. Collaboration among multiple agents can improve work efficiency and has significant practical value. However, most of the existing research focuses on homogeneous multi-agent tasks. Compared with homogeneous agents, heterogeneous agents can leverage their different capabilities to allocate corresponding sub-tasks and cooperate to complete complex tasks. Heterogeneous multi-agent tasks are common in real-world scenarios, and the collaboration strategy among heterogeneous agents is a challenging and important problem to be solved. To study collaboration among heterogeneous agents, we propose the heterogeneous multi-agent tidying-up task, in which multiple heterogeneous agents with different capabilities collaborate with each other to detect misplaced objects and place them in reasonable locations. This is a demanding task since it requires agents to make the best use of their different capabilities to conduct reasonable task planning and complete the whole task. To solve this task, we build a heterogeneous multi-agent tidying-up benchmark dataset in a large number of houses with multiple rooms based on ProcTHOR-10K. We propose the hierarchical decision model based on misplaced object detection, reasonable receptacle prediction, as well as the handshake-based group communication mechanism. Extensive experiments are conducted to demonstrate the effectiveness of the proposed model. The project's website and videos of experiments can be found at https://hetercol.github.io/.