
Dobb·E
Empowering Homes with Versatile Robotic Assistance

Dobb·E is an open-source, general framework for learning household robotic manipulation. It aims to create a 'generalist machine' for homes, a domestic assistant that can adapt and learn various tasks cost-effectively. Dobb·E can learn a new task with just five minutes of demonstration, achieving an 81% success rate in 10 NYC homes. The system is designed to accelerate research on home robots and eventually enable robot butlers in every home.
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Features
- Open-source framework for household robotic manipulation
- Rapid learning of new tasks with minimal demonstration time
- Achieves an 81% success rate in various household environments
- Utilizes Home Pretrained Representations (HPR) for task performance
- Accessible hardware design for demonstration data collection
Advantages
- Cost-effective solution for household robotic manipulation
- Versatile and adaptable to different tasks and environments
- Accelerates research on home robotics
- Promotes the development of generalist machines for domestic use
- Open-source software stack, models, data, and hardware designs
Disadvantages
- Dependence on demonstration data quality by non-expert users
- Challenges with strong shadows affecting task performance
- Limited to household manipulation tasks
Frequently Asked Questions
-
Q:What is Dobb·E?
A:Dobb·E is an open-source framework for learning household robotic manipulation. -
Q:How long does it take for Dobb·E to learn a new task?
A:Dobb·E can learn a new task with just five minutes of demonstration. -
Q:What is the success rate of Dobb·E in NYC homes?
A:Dobb·E achieves an 81% success rate in 10 NYC homes.
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