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

• URL: http://arxiv.org/abs/2401.17914v1
• Date: Wed, 31 Jan 2024 15:24:13 GMT
• Title: Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning
• Authors: Erwan Escudie and Laetitia Matignon and Jacques Saraydaryan
• Abstract summary: We present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using deep reinforcement learning (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) 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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