📂 Assistants Personnels 👁 3.5k views 🕐 May 29, 2026

Mobile ALOHA

Mobile ALOHA is a low-cost and whole-body teleoperation system for data collection,.

Mobile ALOHA is a low-cost and whole-body teleoperation system for data collection, developed by Zipeng Fu, Tony Z. Zhao, and Chelsea Finn at Stanford. It is designed for imitating mobile manipulation tasks that are bimanual and require whole-body control. Mobile ALOHA augments the ALOHA system with a mobile base and a whole-body teleoperation interface, allowing for the collection of data on complex tasks such as sauteing and serving food, or opening a two-door wall cabinet.
The system uses supervised behavior cloning and co-training with existing static ALOHA datasets to boost performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, enabling Mobile ALOHA to autonomously complete complex tasks.
Researchers and developers in the field of robotics, particularly those focusing on autonomous mobile manipulation, can greatly benefit from Mobile ALOHA. It provides a low-cost solution for data collection and enables the imitation of complex tasks, making it an invaluable tool for advancing research in this area.

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Features
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Low-cost whole-body teleoperation
allows for the collection of data on complex mobile manipulation tasks
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Mobile base
enables the system to move around and interact with its environment
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Whole-body teleoperation interface
provides a way for humans to demonstrate tasks to the system
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Supervised behavior cloning
enables the system to learn from human demonstrations
Verdict
Best forTeams doing Assistants Personnels work who need consistent output without a steep learning curve.
Skip ifYou only need this once or twice; the subscription cost won't pay off for occasional use.
✓Enables autonomous completion of complex mobile manipulation tasks
✓Low-cost solution for data collection
✓Boosts success rates by up to 90% with co-training
✕Requires 50 demonstrations for each task to achieve high success rates
✕Limited to tasks that can be demonstrated by a human operator
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Frequently Asked Questions
Mobile ALOHA is a low-cost whole-body teleoperation system for data collection, developed by Zipeng Fu, Tony Z. Zhao, and Chelsea Finn at Stanford. It is designed for imitating mobile manipulation tasks that are bimanual and require whole-body control.
Mobile ALOHA uses supervised behavior cloning and co-training with existing static ALOHA datasets to boost performance on mobile manipulation tasks. It requires 50 demonstrations for each task to achieve high success rates.
Mobile ALOHA enables autonomous completion of complex mobile manipulation tasks, provides a low-cost solution for data collection, and boosts success rates by up to 90% with co-training.
Mobile ALOHA requires 50 demonstrations for each task to achieve high success rates and is limited to tasks that can be demonstrated by a human operator.
Mobile ALOHA is a low-cost solution that provides a whole-body teleoperation interface and co-training with existing datasets, making it a unique and valuable tool for researchers and developers in the field of autonomous robotics.
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Mobile ALOHA
Mobile ALOHA
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