What hardware do I need to use Dobb-E?

You need a Hello Robot Stretch mobile manipulator, an iPhone with LiDAR (for 'The Stick'), and specific 3D-printed parts and a reacher-grabber stick for data collection.

Is Dobb-E a commercial product?

No, Dobb-E is currently a research project and open-source framework intended for developers and researchers to advance home robotics.

How long does it take to teach a robot a new task?

It takes approximately 5 minutes of manual demonstration using 'The Stick' and 15 minutes of computational fine-tuning.

What kind of tasks can Dobb-E perform?

It has been tested on over 109 tasks, including opening drawers, flicking light switches, and moving household objects.

Where can I find the code and models?

The software is available on GitHub (notmahi/dobb-e) and the pretrained models are hosted on Hugging Face.

Does it require a constant internet connection?

The deployment of learned policies typically runs locally on the robot's hardware, though initial setup and model downloading require internet access.

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Robotics

Free

Dobb-E

An open-source framework to teach robots household tasks in 20 minutes using an iPhone-based demonstration tool and pretrained models.

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Rating

Reviews

Upvotes

3 months ago

Listed

Robotics

Open Source

Artificial Intelligence

Machine Learning

Home Automation

Computer Vision

Research Tool

Tool information

Provider

NYU / Meta AI

Platforms

Web

Linux

iOS (for data collection)

Languages

English

Added to directory

3 months ago

Last updated

about 1 month ago

Rating

Not available

Pricing

Free

About Dobb-E

What the tool does and who it's for

Dobb-E is an open-source robotic framework designed to bridge the gap between laboratory research and real-world household automation. Developed by researchers at NYU and Meta, it utilizes a specialized data collection tool called 'The Stick'—constructed from affordable components like a reacher-grabber and an iPhone—to record human demonstrations. By leveraging the 'Homes of New York' (HoNY) dataset and Home Pretrained Representations (HPR), Dobb-E allows users to teach a mobile robot (specifically the Hello Robot Stretch) new household tasks, such as opening drawers or turning off lights, in approximately 20 minutes (5 minutes of demonstration followed by 15 minutes of fine-tuning).

Key capabilities

The Stick: A low-cost demonstration tool using an iPhone and 3D-printed parts.

Home Pretrained Representations (HPR): A vision model pretrained on diverse home environments.

Rapid Learning: Teach a new robotic task in just 20 minutes.

HoNY Dataset: Access to 13 hours of real-world household interaction data.

Open Source: Fully accessible software stack, models, and hardware designs.

Real-world Robustness: Designed to handle household challenges like shadows and variable lighting.

Compatibility: Specifically optimized for the Hello Robot Stretch platform.

Behavior Cloning: Uses imitation learning to map visual inputs to robotic actions.

Pricing

Free — plans below

Open Source

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