[R] DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

**Paper**: [https://arxiv.org/abs/2403.12945](https://arxiv.org/abs/2403.12945)

**Project page**: [https://droid-dataset.github.io/](https://droid-dataset.github.io/)

**Hardware code**: [https://github.com/droid-dataset/droid](https://github.com/droid-dataset/droid)

**Policy learning code**: [https://github.com/droid-dataset/droid\_policy\_learning](https://github.com/droid-dataset/droid_policy_learning)

**Dataset Colab**: [https://colab.research.google.com/drive/1b4PPH4XGht4Jve2xPKMCh-AXXAQziNQa?usp=sharing](https://colab.research.google.com/drive/1b4PPH4XGht4Jve2xPKMCh-AXXAQziNQa?usp=sharing)

**Abstract**:

>The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are …

abstract challenges data datasets diverse environments general hardware however human investments labour machinelearning manipulation path policies quality robot robotic robotic manipulation robot manipulation robust safety stone