RoboFactory: Exploring Embodied Agent Collaboration with Compositional
  Constraints
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.
67dcdedbeff29d0d52c739e4
Yiran Qin
claimed_verified
2025-03-21T11:40:36.686Z
658a6c1399ed106ac8c822b1
https://cdn-avatars.huggingface.co/v1/production/uploads/658a6c1399ed106ac8c822b1/Wk2KXCcK39rUvXx6mpmGD.jpeg
yiranqin
user
IranQin
67dcdedbeff29d0d52c739e5
Li Kang
claimed_verified
2025-03-21T11:40:38.834Z
64eadcb03d76028d805a7818
/avatars/528e4fded4419caf08589b2ed40437bc.svg
Li Kang
user
FACEONG
67dcdedbeff29d0d52c739e6
Xiufeng Song
admin_assigned
2025-03-24T12:35:50.462Z
64cdf8230fbfb00b91225087
/avatars/5379727f55782e146302ed20c7661932.svg
Xiufeng Song
user
sparklexfantasy
67dcdedbeff29d0d52c739e7
Zhenfei Yin
admin_assigned
2025-03-24T12:35:56.882Z
64e314ad24809d7fa0f20fbc
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/bHE0w_hjDFvU-Aul0_E7g.jpeg
Zhenfei Yin
user
JeremyYin
67dcdedbeff29d0d52c739e8
Xiaohong Liu
67dcdedbeff29d0d52c739e9
Xihui Liu
admin_assigned
2025-03-24T12:36:19.727Z
65d5ec74cd05bc1eaa125040
/avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
Xihui Liu
user
XihuiLiu
67dcdedbeff29d0d52c739ea
Ruimao Zhang
admin_assigned
2025-03-24T12:37:18.101Z
65324848220f490dbaa3b8c9
/avatars/cec7dcadf4657bb4f61e2c9a8075521b.svg
Zhang
user
Ruimao
67dcdedbeff29d0d52c739eb
Lei Bai
RoboFactory: Exploring Embodied Agent Collaboration with Compositional
  Constraints
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.
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.
embodied multi-agent systems compositional constraints imitation learning RoboFactory benchmark multi-agent imitation learning