Dobb·E – towards home robots with an open-source platform
Hi HN! Proud to share our open-source robot platform, Dobb·E, a home robot system that needs just 5 minutes of human teaching to learn new tasks. We've already taken Dobb·E to 10 different homes in New York, taught it 100+ tasks, and we are just getting started! I would love to hear your thoughts about this. Here are some more details, below (or see a Twitter thread with attached media: https://twitter.com/i/status/1729515379892826211 or https://nitter.net/i/status/1729515379892826211): We engineered Dobb·E to maximize efficiency, safety, and…
In plain words
Dobb·E is an open-source home robot platform that learns new household tasks from just five minutes of human demonstration. The system uses imitation learning with a data collection tool called "the Stick," along with a pretrained vision model and fine-tuning recipe. Designed for efficiency and safety, Dobb·E has been tested in multiple homes and taught over 100 different tasks. It is for researchers and developers interested in robotics and household automation.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! Proud to share our open-source robot platform, Dobb·E, a home robot system that needs just 5 minutes of human teaching to learn new tasks. We've already taken Dobb·E to 10 different homes in New York, taught it 100+ tasks, and we are just getting started! I would love to hear your thoughts about this. Here are some more details, below (or see a Twitter thread with attached media: https://twitter.com/i/status/1729515379892826211 or https://nitter.net/i/status/1729515379892826211): We engineered Dobb·E to maximize efficiency, safety, and user comfort. As a system, it is composed of four parts: a data collection tool, a home dataset, a pretrained vision model, and a policy fine-tuning recipe. We teach our robots with imitation learning, and for data collection, we created the “Stick”, a tool made out of $25 of hardware and an iPhone. Then, using the Stick, we collected a 13 hour dataset in 22 New York homes, called Homes of New York (HoNY). HoNY has 1.5M frames collected over 216 different "environments" which is an order of magnitude larger compared to similar open source datasets. Then we trained a foundational vision model that we can fine-tune fast (15 minutes!) on a new task with only 5 minutes (human time)/ 90 seconds (demo time) of data. So from start to finish, it takes about 20 minutes to teach the robot a new task. Over a month, we visited 10 homes, tried 109 tasks, and got 81% success rate in simple household tasks. We also found a line of challenges, from mirrors to heavy objects, that we must overcome if we are to get a general purpose home robot. We open-sourced our entire system because our primary goal is to get more robotics and AI researchers, engineers, and enthusiasts to go beyond constrained lab environments and start getting into homes! So here is how you can get started: 1. Code and STL files: https://github.com/notmahi/dobb-e/ 2. Technical documentation: https://docs.dobb-e.com/ 3. Paper: https://arxiv.org/abs/2311.16098 4. More videos and the dataset: https://dobb-e.com 5. Robot we used: https://hello-robot.com
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