Dobb·E

Dobb·E teaches robots household tasks in 20 minutes through imitation learning and open-source frameworks.
July 24, 2024
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Dobb·E Website

About Dobb·E

Dobb·E is an open-source framework dedicated to teaching robots household tasks through imitation learning. Targeting home robotics enthusiasts and developers, it enables robots to learn new tasks within 20 minutes, utilizing a unique tool called The Stick. Its innovative approach addresses the challenges of home robotics effectively.

Dobb·E offers a free open-source solution for researchers and developers. While it is available at no cost, donations may be encouraged to support ongoing development. The open-source nature provides users with full access to datasets, models, and features, making it a valuable resource for robotics innovation.

Dobb·E’s user-friendly interface ensures smooth navigation and easy access to its powerful features, allowing users to seamlessly integrate robotic task learning into their projects. The layout is designed for accessibility, making it intuitive for both beginners and experienced developers interested in home robotics advancements.

How Dobb·E works

Users interact with Dobb·E by utilizing The Stick, a demonstration collection tool, to collect task demonstrations in their home environments. After data collection, users train the Home Pretrained Representations model, which adapts to new tasks using a simple five-minute demonstration. The process is efficient, making robotic learning accessible to everyone.

Key Features for Dobb·E

Imitation Learning for Robots

Dobb·E’s core feature is its imitation learning capability, allowing robots to rapidly learn household tasks. By leveraging a simple tool and a unique dataset, users can train robots to adapt quickly to new environments, achieving remarkable success rates. This innovative approach transforms how household robotics are developed.

Homes of New York Dataset

One standout feature of Dobb·E is the Homes of New York (HoNY) dataset, which comprises 13 hours of diverse household interaction data. This extensive dataset enhances the training of robotic models, ensuring adaptability and effective learning, thus providing significant value for developers and researchers in the field.

The Stick Demonstration Tool

The Stick is a unique, cost-effective tool designed to collect task demonstrations effortlessly. This innovative device, made from simple materials, facilitates the imitation learning process for robots, making it easier for users to teach new tasks, thereby enhancing the overall efficiency and accessibility of robotic learning.

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