6 robots, 24 hours: AgiBot's convenience store test moves humanoids closer to real deployment
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6 robots, 24 hours: AgiBot's convenience store test moves humanoids closer to real deployment
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The “Robots Can’t Stop Working” livestream concluded Tuesday.
Six AgiBot G2 humanoid robots completed 1,730 tasks over 24 continuous hours in a live-streamed convenience store challenge, logging 683 customer receptions, 369 item deliveries, 147 coffee preparations, 126 breakfast services, 277 restocking and inspection rounds, and 128 table cleanings.
The six robots were assigned different roles and worked consecutive shifts using AgiBot’s newly released GO-3 Preview large model and Genie Master framework.
GO-3 serves as the “brain,” drawing on large-scale data to jointly interpret vision, language, environmental state and task instructions, then reason about what to do, how to do it, and generate concrete actions.
Genie Master is the embodied agent framework — the convenience store robots’ central coordinator, responsible for on-site scheduling, exception recovery and experience feedback.
The live test also exposed small bugs.
After picking up goods, a robot’s delivery navigation could become prolonged once blocked, as it replanned routes and resumed delivery — a sign of limited adaptability.
The deployment took about three months, faster than previous entries into factory settings.
Factories involve repetitive motions, while convenience stores require long-horizon task capability and robot coordination; the store was chosen to validate the model and framework in real operations.
Four of the six robots passed the “regularization” assessment.
They will not immediately be assigned to convenience stores; instead, the successful and failed experiences will flow back into the training system for replication in more stores and scenarios.
Another focus of the public validation was collecting material from real scenarios.
The industry significance of embodied intelligence is that once one robot learns a task, 10,000 robots can adopt it immediately.
In the future, a convenience store may not need six robots — one or two could handle the store’s entire workload, lowering operating costs and the barrier to deployment.
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Six AgiBot G2 humanoid robots completed 1,730 tasks over 24 continuous hours in a live-streamed convenience store challenge, logging 683 customer receptions, 369 item deliveries, 147 coffee preparations, 126 breakfast services, 277 restocking and inspection rounds, and 128 table cleanings.
The six robots were assigned different roles and worked consecutive shifts using AgiBot’s newly released GO-3 Preview large model and Genie Master framework.
GO-3 serves as the “brain,” drawing on large-scale data to jointly interpret vision, language, environmental state and task instructions, then reason about what to do, how to do it, and generate concrete actions.
Genie Master is the embodied agent framework — the convenience store robots’ central coordinator, responsible for on-site scheduling, exception recovery and experience feedback.
The live test also exposed small bugs.
After picking up goods, a robot’s delivery navigation could become prolonged once blocked, as it replanned routes and resumed delivery — a sign of limited adaptability.
The deployment took about three months, faster than previous entries into factory settings.
Factories involve repetitive motions, while convenience stores require long-horizon task capability and robot coordination; the store was chosen to validate the model and framework in real operations.
Four of the six robots passed the “regularization” assessment.
They will not immediately be assigned to convenience stores; instead, the successful and failed experiences will flow back into the training system for replication in more stores and scenarios.
Another focus of the public validation was collecting material from real scenarios.
The industry significance of embodied intelligence is that once one robot learns a task, 10,000 robots can adopt it immediately.
In the future, a convenience store may not need six robots — one or two could handle the store’s entire workload, lowering operating costs and the barrier to deployment.
__________________
ShanghaiEye focuses on producing top-quality contents.
Nobody knows SHANGHAI better than us.
Please subscribe to us ☻☻☻
__________________
For more stories, please click
■ What's up today in Shanghai, the most updated news of the city
https://www.youtube.com/playlist?list=PLeyl7CQ2A5MNjpE4hspSVry8dR555getN
■ Amazing Shanghai, exploring the unknown corners of the city, learning the people, food and stories behind them
https://www.youtube.com/playlist?list=PLeyl7CQ2A5MMVTWr-zIXjsS4hhSQPmyes
■ What Chinese people's lives are like during the post COVID-19 period
https://www.youtube.com/playlist?list=PLeyl7CQ2A5MPrPTmIirpfgETu6VKVESXe
■ Views of foreign scholars on China and its affairs
https://www.youtube.com/playlist?list=PLeyl7CQ2A5MO9ua_nSK6slRPdUl-ovjRe
■ Foreign faces in Shanghai, people living in this city sharing their true feelings
https://www.youtube.com/playlist?list=PLeyl7CQ2A5MP0vJDSw23_1MCPJhAfGbiG
■ Mini-docs showing why China is the country it is today
https://www.youtube.com/playlist?list=PLeyl7CQ2A5MOreRu2AbZ3ziIUyCcdBLwX
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☎Leave us messages if you have any suggestions or questions!
Thank you!
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