Learning 1000 Robot Manipulation Tasks in a Day (Code and Data)

In this repository, we are providing the code to replicate all results published in the Science Robotics paper: "Learning 1000 Tasks in a Day". Together with the code, you will find tutorials on how to run and train the different robot learning methods. Additionally, we also share the weights of the trained models we deemed useful and generalisable to various robot setups. Finally, we include data collected directly on the robot. These are robot rollouts that were labelled as either success or failure, and for which we share data such as RGB-D images, joint angles, gripper states, and all other pertinent information that can be valuable to the community for model training.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-13
DOI
https://doi.org/10.5281/zenodo.17334510
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Learning 1000 Robot Manipulation Tasks in a Day (Code and Data)

Zenodo (CERN European Organization for Nuclear Research)
Robot Manipulation and Learning
article

Learning 1000 Robot Manipulation Tasks in a Day (Code and Data)

article en

Abstract

In this repository, we are providing the code to replicate all results published in the Science Robotics paper: "Learning 1000 Tasks in a Day". Together with the code, you will find tutorials on how to run and train the different robot learning methods. Additionally, we also share the weights of the trained models we deemed useful and generalisable to various robot setups. Finally, we include data collected directly on the robot. These are robot rollouts that were labelled as either success or failure, and for which we share data such as RGB-D images, joint angles, gripper states, and all other pertinent information that can be valuable to the community for model training.

Zenodo (CERN European Organization for Nuclear Research)
Imperial College London (GB)
Quality Education
Openalex Percentile: Top 43%
Robot Manipulation and Learning
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