MOSAIC: A Modular System for Assistive and Interactive Cooking
We present MOSAIC, a modular architecture for home robots to perform complex
collaborative tasks, such as cooking with everyday users. MOSAIC tightly
collaborates with humans, interacts with users using natural language,
coordinates multiple robots, and manages an open vocabulary of everyday
objects. At its core, MOSAIC employs modularity: it leverages multiple
large-scale pre-trained models for general tasks like language and image
recognition, while using streamlined modules designed for task-specific
control. We extensively evaluate MOSAIC on 60 end-to-end trials where two
robots collaborate with a human user to cook a combination of 6 recipes. We
also extensively test individual modules with 180 episodes of visuomotor
picking, 60 episodes of human motion forecasting, and 46 online user
evaluations of the task planner. We show that MOSAIC is able to efficiently
collaborate with humans by running the overall system end-to-end with a real
human user, completing 68.3% (41/60) collaborative cooking trials of 6
different recipes with a subtask completion rate of 91.6%. Finally, we discuss
the limitations of the current system and exciting open challenges in this
domain. The project's website is at https://portal-cornell.github.io/MOSAIC/
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Huaxiaoyue Wang
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Yuki Wang
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Kushal Kedia
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Kushal Kedia
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Juntao Ren
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Juntao Ren
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Rahma Abdullah
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Angela Chao
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Angela Chao
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Kelly Y Chen
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Nathaniel Chin
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Nathaniel Chin
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Prithwish Dan
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Gonzalo Gonzalez-Pumariega
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Maximus Adrian Pace
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Yash Sharma
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Yash Sharma
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Xiangwan Sun
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Neha Sunkara
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Neha Sunkara
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Sanjiban Choudhury
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Sanjiban Choudhury
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MOSAIC: A Modular System for Assistive and Interactive Cooking
We present MOSAIC, a modular architecture for home robots to perform complex
collaborative tasks, such as cooking with everyday users. MOSAIC tightly
collaborates with humans, interacts with users using natural language,
coordinates multiple robots, and manages an open vocabulary of everyday
objects. At its core, MOSAIC employs modularity: it leverages multiple
large-scale pre-trained models for general tasks like language and image
recognition, while using streamlined modules designed for task-specific
control. We extensively evaluate MOSAIC on 60 end-to-end trials where two
robots collaborate with a human user to cook a combination of 6 recipes. We
also extensively test individual modules with 180 episodes of visuomotor
picking, 60 episodes of human motion forecasting, and 46 online user
evaluations of the task planner. We show that MOSAIC is able to efficiently
collaborate with humans by running the overall system end-to-end with a real
human user, completing 68.3% (41/60) collaborative cooking trials of 6
different recipes with a subtask completion rate of 91.6%. Finally, we discuss
the limitations of the current system and exciting open challenges in this
domain. The project's website is at https://portal-cornell.github.io/MOSAIC/
MOSAIC, a modular home robot architecture, collaborates with humans to perform complex cooking tasks using pre-trained models and specific modules, achieving 68.3% success in 60 collaborative trials with various recipes.
modular architecture home robots complex collaborative tasks natural language language recognition image recognition task-specific control end-to-end trials visuomotor picking human motion forecasting task planner subtask completion rate