Dobb·E
Empowering Home Robotics
Dobb·E is an open-source, general framework for learning household robotic manipulation. It aims to create a 'generalist machine' for homes that can adapt and learn various tasks cost-effectively. Dobb·E can learn a new task in just five minutes of demonstration, thanks to a tool called 'The Stick' for data collection. The system achieved an 81% success rate in completing 109 tasks across 10 homes in New York City. Dobb·E is designed to accelerate research on home robots and make robot assistants a common sight in households.
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Features
- Open-source framework
- Rapid task learning
- Affordable and versatile
- Demonstration collection tool
- Home Pretrained Representations model
Advantages
- Quick task adaptation
- High success rate
- Cost-effective solution
- Large-scale experimentation
- Open-source for research
Disadvantages
- Dependence on user demonstrations
- Challenges with non-expert users
- Variable demonstration quality
Frequently Asked Questions
-
Q:How long does it take for Dobb·E to learn a new task?
A:Dobb·E can learn a new task in just five minutes of demonstration. -
Q:What is the success rate of Dobb·E in completing tasks?
A:Dobb·E achieved an 81% success rate in completing 109 tasks. -
Q:Is Dobb·E suitable for research purposes?
A:Yes, Dobb·E is open-source and designed to accelerate research on home robots.
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Dobb·E is an open-source, general framework for learning household robotic manipulation. It aims to create a 'generalist machine' for homes that can adapt and learn various tasks cost-effectively. Dobb·E can learn a new task in just five minutes of demonstration, thanks to a tool called 'The Stick' for data collection. The system achieved an 81% success rate in completing 109 tasks across 10 homes in New York City. Dobb·E is designed to accelerate research on home robots and make robot assistants a common sight in households.
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