Two Body Problem: Collaborative Visual Task Completion

April 11, 2019 · Entered Twilight · 🏛 Computer Vision and Pattern Recognition

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Repo contents: .gitignore, LICENSE, Pipfile, README.md, ai2thor_builds, analysis_output, autoformat.sh, constants.py, data, images, logs, requirements.txt, rl_ai2thor, rl_base, rl_multi_agent, trained_models, utils

Authors Unnat Jain, Luca Weihs, Eric Kolve, Mohammad Rastegari, Svetlana Lazebnik, Ali Farhadi, Alexander Schwing, Aniruddha Kembhavi arXiv ID 1904.05879 Category cs.CV: Computer Vision Cross-listed cs.AI, cs.MA Citations 75 Venue Computer Vision and Pattern Recognition Repository https://github.com/allenai/cordial-sync ⭐ 41 Last Checked 8 days ago

Abstract

Collaboration is a necessary skill to perform tasks that are beyond one agent's capabilities. Addressed extensively in both conventional and modern AI, multi-agent collaboration has often been studied in the context of simple grid worlds. We argue that there are inherently visual aspects to collaboration which should be studied in visually rich environments. A key element in collaboration is communication that can be either explicit, through messages, or implicit, through perception of the other agents and the visual world. Learning to collaborate in a visual environment entails learning (1) to perform the task, (2) when and what to communicate, and (3) how to act based on these communications and the perception of the visual world. In this paper we study the problem of learning to collaborate directly from pixels in AI2-THOR and demonstrate the benefits of explicit and implicit modes of communication to perform visual tasks. Refer to our project page for more details: https://prior.allenai.org/projects/two-body-problem