A new open-source system called Dobb-E can train robots for domestic tasks using real home data, addressing the lack of training data in robotics. Utilizing an iPhone and reacher-grabber stick to collect data, the system achieved an 81% success rate in executing household tasks over 30 days. The team aims to expand Dobb-E’s capabilities.
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A New System to Teach Robots Domestic Tasks in 20 Minutes
A new open-source system called Dobb-E has been developed to train robots to perform domestic tasks using data collected from real homes. This system addresses the challenge of a lack of training data for robots and the difficulty of replicating real home conditions in laboratory settings.
Practical Solutions and Value
The team used a simple method to collect the necessary data for training Dobb-E, involving an iPhone attached to a reacher-grabber stick to record videos of household tasks. This data was then used to train an AI model to instruct a robot in carrying out these actions.
The commercially available robot, Stretch, was tested in 10 homes and successfully completed 109 household tasks with an overall success rate of 81%. The system was able to learn tasks in around 20 minutes and could perform various actions such as pouring from a cup, opening blinds, and more.
While not every task was successful, the research represents tangible progress for the home robotics field, addressing the challenge of scaling up data for training robots.
The project has been made open source, and the team hopes to expand the range of tasks that robots running Dobb-E can achieve.
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