title: RoboFactory: Exploring Embodied Agent Collaboration with Compositional
    Constraints
summary: Designing effective embodied multi-agent systems is critical for solving
  complex real-world tasks across domains. Due to the complexity of multi-agent
  embodied systems, existing methods fail to automatically generate safe and
  efficient training data for such systems. To this end, we propose the concept
  of compositional constraints for embodied multi-agent systems, addressing the
  challenges arising from collaboration among embodied agents. We design various
  interfaces tailored to different types of constraints, enabling seamless
  interaction with the physical world. Leveraging compositional constraints and
  specifically designed interfaces, we develop an automated data collection
  framework for embodied multi-agent systems and introduce the first benchmark
  for embodied multi-agent manipulation, RoboFactory. Based on RoboFactory
  benchmark, we adapt and evaluate the method of imitation learning and analyzed
  its performance in different difficulty agent tasks. Furthermore, we explore
  the architectures and training strategies for multi-agent imitation learning,
  aiming to build safe and efficient embodied multi-agent systems.
paper_authors._id: 67dcdedbeff29d0d52c739e4
paper_authors.name: Yiran Qin
paper_authors.status: claimed_verified
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paper_authors.fullname: yiranqin
paper_authors.type: user
paper_authors.user: IranQin
paper_authors._id: 67dcdedbeff29d0d52c739e5
paper_authors.name: Li Kang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/528e4fded4419caf08589b2ed40437bc.svg
paper_authors.fullname: Li Kang
paper_authors.type: user
paper_authors.user: FACEONG
paper_authors._id: 67dcdedbeff29d0d52c739e6
paper_authors.name: Xiufeng Song
paper_authors.status: admin_assigned
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paper_authors._id: 64cdf8230fbfb00b91225087
paper_authors.avatarUrl: /avatars/5379727f55782e146302ed20c7661932.svg
paper_authors.fullname: Xiufeng Song
paper_authors.type: user
paper_authors.user: sparklexfantasy
paper_authors._id: 67dcdedbeff29d0d52c739e7
paper_authors.name: Zhenfei Yin
paper_authors.status: admin_assigned
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paper_authors._id: 64e314ad24809d7fa0f20fbc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/bHE0w_hjDFvU-Aul0_E7g.jpeg
paper_authors.fullname: Zhenfei Yin
paper_authors.type: user
paper_authors.user: JeremyYin
paper_authors._id: 67dcdedbeff29d0d52c739e8
paper_authors.name: Xiaohong Liu
paper_authors._id: 67dcdedbeff29d0d52c739e9
paper_authors.name: Xihui Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:36:19.727Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_authors._id: 67dcdedbeff29d0d52c739ea
paper_authors.name: Ruimao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:37:18.101Z
paper_authors._id: 65324848220f490dbaa3b8c9
paper_authors.avatarUrl: /avatars/cec7dcadf4657bb4f61e2c9a8075521b.svg
paper_authors.fullname: Zhang
paper_authors.type: user
paper_authors.user: Ruimao
paper_authors._id: 67dcdedbeff29d0d52c739eb
paper_authors.name: Lei Bai
paper_title: RoboFactory: Exploring Embodied Agent Collaboration with Compositional
    Constraints
paper_summary: Designing effective embodied multi-agent systems is critical for solving
  complex real-world tasks across domains. Due to the complexity of multi-agent
  embodied systems, existing methods fail to automatically generate safe and
  efficient training data for such systems. To this end, we propose the concept
  of compositional constraints for embodied multi-agent systems, addressing the
  challenges arising from collaboration among embodied agents. We design various
  interfaces tailored to different types of constraints, enabling seamless
  interaction with the physical world. Leveraging compositional constraints and
  specifically designed interfaces, we develop an automated data collection
  framework for embodied multi-agent systems and introduce the first benchmark
  for embodied multi-agent manipulation, RoboFactory. Based on RoboFactory
  benchmark, we adapt and evaluate the method of imitation learning and analyzed
  its performance in different difficulty agent tasks. Furthermore, we explore
  the architectures and training strategies for multi-agent imitation learning,
  aiming to build safe and efficient embodied multi-agent systems.
paper_ai_summary: Compositional constraints and tailored interfaces are introduced to automate data collection and improve imitation learning in embodied multi-agent systems, enabling the development of safe and efficient manipulation tasks.
paper_ai_keywords: embodied multi-agent systems compositional constraints imitation learning RoboFactory benchmark multi-agent imitation learning