How can robots predict worker actions using decentralized graph networks?
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
January 9, 2025
https://arxiv.org/pdf/2501.04193This research presents a decentralized system for multiple robots to predict human worker actions in industrial settings. Each robot builds and shares a spatial understanding of its surroundings (represented as a graph of objects and human) with other robots. This information, along with temporal data about human pose, is fed into recurrent neural networks (RNNs) for individual and collective intent prediction. A consensus mechanism, inspired by swarm intelligence, allows the robots to converge on a unified prediction, improving accuracy and robustness.
Key points relevant to LLM-based multi-agent systems:
- Decentralized communication and collaboration: Robots share spatial and temporal information to form a shared understanding. This mirrors the way LLMs could share knowledge and context in a multi-agent system.
- Consensus mechanisms: The swarm-inspired consensus algorithm used to combine predictions from multiple robots is directly applicable to LLM agents that need to reach agreement on a course of action.
- Spatial and temporal reasoning: The system’s combined use of graph neural networks (GNNs) for spatial relationships and RNNs for temporal dynamics is relevant to LLMs, which can also benefit from understanding spatial and temporal contexts.
- Robustness and fault tolerance: The decentralized approach improves system robustness by compensating for individual robot failures. This is a critical consideration for LLM-based multi-agent systems as well.