  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 mostly trained on data collected in a small number of environments with
limited scene and task diversity. In this work, we introduce DROID (Distributed
Robot Interaction Dataset), a diverse robot manipulation dataset with 76k
demonstration trajectories or 350 hours of interaction data, collected across
564 scenes and 84 tasks by 50 data collectors in North America, Asia, and
Europe over the course of 12 months. We demonstrate that training with DROID
leads to policies with higher performance and improved generalization ability.
We open source the full dataset, policy learning code, and a detailed guide for
reproducing our robot hardware setup.
