DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Bibliographic record
Abstract
Dining RoomBedroom Laboratory Laundry Room Office Kitchen Fig. 1: We introduce DROID (Distributed Robot Interaction Dataset), an "in-the-wild" robot manipulation dataset with 76k trajectories or 350 hours of interaction data, collected across 564 scenes, 86 tasks, and 52 buildings over the course of 12 months.Each DROID episode contains three synchronized RGB camera streams, camera calibration, depth information, and natural language instructions.We demonstrate that training with DROID leads to policies with higher performance, greater robustness, and improved generalization ability.We open source the full dataset, pre-trained model checkpoints, and a detailed guide for reproducing our robot setup.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 * Project Co-leads, correspondence to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".