DecisionFacts/Physical_AI_SO101_Towel_Folding_Task

DecisionFacts Physical AI Dataset — SO-101 Robotic Arm Teleoperation

Data Summary

This dataset is a curated collection of real-world teleoperation data captured on the SO-101 robotic arm (so_follower), built to support training and evaluation of modern robot-learning models — from imitation-learning policies to large-scale Vision-Language-Action (VLA) and world models.

Each episode is a human-teleoperated demonstration of a manipulation task, recorded synchronously across two camera viewpoints alongside the arm's full proprioceptive state and control signals. Data collection followed a rigorous, standardized protocol:

• Expert teleoperation — every episode is a deliberate, goal-directed demonstration performed by a trained operator, not scripted or simulated motion.
• Synchronized multi-modal capture — joint states, actions, and dual-camera RGB video are captured in lock-step at 30 fps.
• Consistency checks — episodes are reviewed for completeness, timing alignment, and successful task completion before being included in the released dataset.
• Structured, reproducible recording — all data is captured and stored using the standardized LeRobotDataset v3.0 format, ensuring the dataset is immediately compatible with the broader open robot-learning ecosystem.

Consistency checks — episodes are reviewed for completeness, timing alignment, and coherent execution of the intended task before being included in the released dataset. Outcome (success, failure, or recovery) is recorded per episode rather than used as an inclusion filter.

Why SO-101. SO-101 is the LeRobot ecosystem's own reference low-cost arm, chosen deliberately: it keeps the dataset cheap to reproduce, easy to validate against, and directly compatible with the tooling most open robot-learning research already uses. This release is single-embodiment by design, not by limitation. We do offer custom collection on other embodiments on request. If your work requires cross-embodiment data, reach out — we can scope collection on other hardware platforms.

License & Access

This repository provides a public evaluation sample of the DecisionFacts Physical AI dataset, released under CC BY-NC 4.0 for research and evaluation purposes only. Commercial use, redistribution, or use in training models intended for commercial deployment requires a separate license — see "Licensing the Full Catalog" below.

Access to this repository requires agreeing to the stated terms and providing contact information, which we use solely to follow up about your use case and licensing options. We do not sell or share this information with third parties.

Licensing the Full Catalog

This repository is a sample of a larger, continuously growing dataset. The full catalog includes additional tasks beyond what's shown here, larger per-task episode counts, and can be scoped to specific manipulation skills, environments, or diversity requirements.

We offer:

• Pilot tranches (200–500 episodes) sized for evaluation and fine-tuning experiments
• Production tranches (1,000+ episodes) for training and post-training at scale
• Custom collection for task categories or embodiments not yet in the public catalog

To discuss access to the full catalog or a custom collection scope, contact info@decisionfacts.io.

Citation

If you use this dataset in your work, please cite it as follows:

@misc{decisionfacts_teleops_dataset,
  credits      = {Prabhu Raghav, Sreeram B Unni, Sriram Gopalan, Saravana Kumar},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/DecisionFacts/Physical_AI_SO101_Towel_Folding_Task}}
}