When2com: Multi-Agent Perception via Communication Graph Grouping (2006.00176v2)

Abstract: While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness. It is therefore critical to develop frameworks which support multi-agent collaborative perception in a distributed and bandwidth-efficient manner. In this paper, we address the collaborative perception problem, where one agent is required to perform a perception task and can communicate and share information with other agents on the same task. Specifically, we propose a communication framework by learning both to construct communication groups and decide when to communicate. We demonstrate the generalizability of our framework on two different perception tasks and show that it significantly reduces communication bandwidth while maintaining superior performance.

• The paper introduces a dynamic communication strategy for multi-agent perception, significantly reducing network bandwidth while maintaining high accuracy.
• It employs self-attention mechanisms to form adaptive communication groups, enabling agents to selectively exchange relevant data.
• Experimental results on semantic segmentation and 3D shape classification demonstrate its superior performance over fully connected models.

Analyzing "When2com: Multi-Agent Perception via Communication Graph Grouping"

The paper "When2com: Multi-Agent Perception via Communication Graph Grouping" proposes a communication framework designed for multi-agent systems engaged in collaborative perception tasks. Given the increasing relevance of multi-agent systems in applications such as robotics, the main contribution lies in optimizing communication strategies to enhance perception tasks while observing bandwidth constraints.

Problem Context and Contributions

In multi-agent systems, individual agents equipped with local sensors often need to collaborate to achieve tasks like object detection, segmentation, and depth estimation more effectively. The paper identifies a critical challenge in this domain: maintaining high perception accuracy while minimizing communication bandwidth, which is crucial to avoid network congestion and latency.

The novel contribution of the paper is a two-pronged communication strategy. First, it introduces a method to dynamically construct communication groups, enabling agents to decide with whom to communicate based on perceived relevance. Second, it incorporates a mechanism for deciding when communication is necessary using a self-attention-based approach, avoiding unnecessary communication that can degrade overall performance.

Methodological Approach

The authors implement a learning-based communication model that operates over two primary stages: communication group construction and decision making on the necessity of communication. The architecture leverages attention mechanisms widely utilized in modern neural networks, allowing agents to weigh potential communications only when beneficial. This involves a handshake-like communication protocol to evaluate the relevance of data between potential communicating agents.

The implemented methodology is flexible, applying to various perception tasks—specifically multi-agent semantic segmentation and 3D shape classification. Using asymmetric message sizing, where query sizes are minimized relative to key sizes, the framework reduces bandwidth while maintaining communication efficacy.

Experimental Validation and Results

The results demonstrate the effectiveness of the approach on two curated datasets: a semantic segmentation task using the AirSim-MAP dataset, and a multi-view 3D shape classification task derived from the ModelNet 40 dataset. For both experimental cases, the framework achieves superior performance with reduced bandwidth usage relative to fully connected communication models like TarMac and CommNet.

In particular, the paper details experiments in three settings: Single-Request Multiple-Support (SRMS), Multiple-Request Multiple-Support (MRMS), and Multiple-Request Multiple-Partial-Support (MRMPS), each increasing in complexity and practical applicability. Across these settings, the proposed approach consistently outperformed alternative methods in terms of prediction accuracy and communication efficiency.

Implications and Future Directions

The implications of this research are significant, considering the growing implementation of multi-agent systems in dynamic environments. The dual focus on efficient communication and robust perception aligns well with the performance and resource optimization goals pertinent to real-world applications.

For future work, potential expansions could include:

• Testing in more diverse and variable environments to evaluate resilience and adaptability across different agent densities and observation complexities.
• Exploring further optimizations in query-key mechanisms and incorporating constraints that factor in latency, privacy, and security considerations.

In conclusion, the "When2com" paper advances the field by presenting a structured and scalable approach to optimize communication within multi-agent systems for perception tasks, deftly balancing performance and practicality.