Collaborative Visual Navigation

• URL: http://arxiv.org/abs/2107.01151v1
• Date: Fri, 2 Jul 2021 15:48:16 GMT
• Title: Collaborative Visual Navigation
• Authors: Haiyang Wang, Wenguan Wang, Xizhou Zhu, Jifeng Dai, Liwei Wang
• Abstract summary: We propose a large-scale 3D dataset, CollaVN, for multi-agent visual navigation (MAVN) Diverse MAVN variants are explored to make our problem more general. A memory-augmented communication framework is proposed. Each agent is equipped with a private, external memory to persistently store communication information.
• Score: 69.20264563368762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques. However, previous MARL methods largely focused on grid-world like or game environments; MAS in visually rich environments has remained less explored. To narrow this gap and emphasize the crucial role of perception in MAS, we propose a large-scale 3D dataset, CollaVN, for multi-agent visual navigation (MAVN). In CollaVN, multiple agents are entailed to cooperatively navigate across photo-realistic environments to reach target locations. Diverse MAVN variants are explored to make our problem more general. Moreover, a memory-augmented communication framework is proposed. Each agent is equipped with a private, external memory to persistently store communication information. This allows agents to make better use of their past communication information, enabling more efficient collaboration and robust long-term planning. In our experiments, several baselines and evaluation metrics are designed. We also empirically verify the efficacy of our proposed MARL approach across different MAVN task settings.

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