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.

Related papers

• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning [98.07495732562654]
  offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets. One agent learned by offline MARL often inherits this random policy, jeopardizing the performance of the entire team. We propose a novel framework called Shared Individual Trajectories (SIT) to address this problem.
  arXiv  Detail & Related papers  (2022-11-28T18:11:26Z)
• Interaction Modeling with Multiplex Attention [17.04973256281265]
  We introduce a method for accurately modeling multi-agent systems. We show that our approach outperforms state-of-the-art models in trajectory forecasting and relation inference.
  arXiv  Detail & Related papers  (2022-08-23T00:29:18Z)
• Learning Efficient Multi-Agent Cooperative Visual Exploration [18.42493808094464]
  We consider the task of visual indoor exploration with multiple agents, where the agents need to cooperatively explore the entire indoor region using as few steps as possible. We extend the state-of-the-art single-agent RL solution, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based global-goal planner, Spatial Coordination Planner ( SCP) SCP leverages spatial information from each individual agent in an end-to-end manner and effectively guides the agents to navigate towards different spatial goals with high exploration efficiency.
  arXiv  Detail & Related papers  (2021-10-12T04:48:10Z)
• Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge [42.37872230561632]
  In visual semantic navigation, the robot navigates to a target object with egocentric visual observations and the class label of the target is given. Most of the existing models are only effective for single-agent navigation, and a single agent has low efficiency and poor fault tolerance when completing more complicated tasks. We propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects.
  arXiv  Detail & Related papers  (2021-09-20T13:31:03Z)
• Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially Observable Environments [9.067091068256747]
  We propose a novel network structure called hierarchical graph recurrent network(HGRN) for multi-agent cooperation under partial observability. Based on the above technologies, we proposed a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant named SAC-HRGN.
  arXiv  Detail & Related papers  (2021-09-05T09:51:25Z)
• Multi-Robot Deep Reinforcement Learning for Mobile Navigation [82.62621210336881]
  We propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt) At training time, HInt learns separate perception and dynamics models, and at test time, HInt integrates the two models in a hierarchical manner and plans actions with the integrated model. Our mobile navigation experiments show that HInt outperforms conventional hierarchical policies and single-source approaches.
  arXiv  Detail & Related papers  (2021-06-24T19:07:40Z)
• Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm [0.0]
  We develop a new framework for multi-agent collision avoidance problem. The framework combined traditional pathfinding algorithm and reinforcement learning. In our approach, the agents learn whether to be navigated or to take simple actions to avoid their partners.
  arXiv  Detail & Related papers  (2020-12-05T08:56:58Z)
• Multi-Agent Routing Value Iteration Network [88.38796921838203]
  We propose a graph neural network based model that is able to perform multi-agent routing based on learned value in a sparsely connected graph. We show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
  arXiv  Detail & Related papers  (2020-07-09T22:16:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.