Introduction

Welcome to robot learning in homes with Dobb·E!

Introducing Dobb·E, n open-source, general framework for learning household robotic manipulation.

Welcome to the documentation of Dobb·E!

We want you to to be able to get started with our robot learning framework as fast as possible.

Need more information?

Schedule a call with someone on our team with this link https://calendly.com/mahis/dobb-e or right below.

We will help you get set up as soon as we can.

What is Dobb·E?

Dobb·E is an open-source robotic imitation learning framework that can learn new household tasks in 5 minutes.

Dobb·E is made up of four primary components:

1. A hardware tool, called The Stick, to comfortably collect robotic demonstrations in homes.

2. A dataset, called Homes of New York (HoNY), with 1.5 million RGB-D frames. collected with the Stick across 22 homes and 216 environments of New York City.

3. A pretrained lightweight foundational vision model called Home Pretrained Representations (HPR), trained on the HoNY dataset.

4. Finally, the platform to tie it all together to deploy it in novel homes, where with only five minutes of training data and 15 minutes of fine-tuning HPR, Dobb·E can solve many simple household tasks.

What's in this documentation?

This documentation is meant to help you get started with the system, including

• Setting up your own Stick,

• Collecting your own demonstrations,

• Training a policy from those demonstrations, and

• Running those policies on your own Hello Stretch.

So why wait? Let's get some robots into homes.