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Conference Papers Year : 2024

Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning


Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions between robots and pedestrians, resulting in impressive outcomes especially with recent approaches based on deep reinforcement learning (RL). However, these works do not consider multi-robot scenarios. In this paper, we present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using RL. Inspired by recent works on multi-agent deep RL, our method leverages graph-based representation of agent interactions, combining the positions and fields of view of entities (pedestrians and agents). Each agent uses a model based on two Graph Neural Network combined with attention mechanisms. First an edge-selector produces a sparse graph, then a crowd coordinator applies node attention to produce a graph representing the influence of each entity on the others. This is incorporated into a model-free RL framework to learn multi-agent policies. We evaluate our approach on simulation and provide a series of experiments in a set of various conditions (number of agents / pedestrians). Empirical results show that our method learns faster than social navigation deep RL mono-agent techniques, and enables efficient multi-agent implicit coordination in challenging crowd navigation with multiple heterogeneous humans. Furthermore, by incorporating customizable meta-parameters, we can adjust the neighborhood density to take into account in our navigation strategy.
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hal-04427749 , version 1 (07-02-2024)



  • HAL Id : hal-04427749 , version 1


Erwan Escudie, Laëtitia Matignon, Jacques Saraydaryan. Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning. AAMAS 2024 - International Conference on Autonomous Agents and Multi-Agent Systems, Social Robot Navigation; Multi-Agent; Reinforcement Learning; Multi-Robot Navigation; Graph Neural Networks; Predictive models, May 2024, Auckland, New Zealand. ⟨hal-04427749⟩
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