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.
Institutions
- Imperial College London (GB)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-10-13
- DOI
- https://doi.org/10.5281/zenodo.17334511
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00