Datasets:
id string | name string | startTime int64 | endTime int64 | sampleRate int64 | devices list | snapshots list |
|---|---|---|---|---|---|---|
1762452688623 | recording_1762452551542 | 1,762,452,551,542 | 1,762,452,688,623 | 24 | [{"id":"E4:B3:23:AD:5A:B2","position":"right_hub","connectionId":"Eidon-Tracker-5AB2"},{"id":"E4:B3:(...TRUNCATED) | [{"time":44,"deviceData":{"E4:B3:23:AD:5A:B2":[-0.544921875,0.1546020508,-0.21484375,0.7955932617],"(...TRUNCATED) |
1761879613725 | recording_1761879486384 | 1,761,879,486,384 | 1,761,879,613,725 | 24 | [{"id":"E4:B3:23:AD:5A:B2","position":"right_hub","connectionId":"Eidon-Tracker-5AB2"},{"id":"E4:B3:(...TRUNCATED) | [{"time":54,"deviceData":{"E4:B3:23:AD:5A:B2":[-0.4278564453,0.435546875,-0.5637207031,0.5562744141](...TRUNCATED) |
1762359770630 | recording_1762359578856 | 1,762,359,578,856 | 1,762,359,770,630 | 24 | [{"id":"E4:B3:23:AD:5A:B2","position":"right_hub","connectionId":"Eidon-Tracker-5AB2"},{"id":"E4:B3:(...TRUNCATED) | [{"time":44,"deviceData":{"E4:B3:23:AD:5A:B2":[-0.5126342773,0.1218261719,-0.2333984375,0.8172607422(...TRUNCATED) |
1762309126373 | recording_1762309066885 | 1,762,309,066,885 | 1,762,309,126,373 | 24 | [{"id":"E4:B3:23:AC:75:DA","position":"left_hub","connectionId":"Eidon-Tracker-75DA"},{"id":"E4:B3:2(...TRUNCATED) | [{"time":44,"deviceData":{"E4:B3:23:AC:75:DA":[-0.541809082,0.1747436523,-0.3278198242,0.7539672852](...TRUNCATED) |
1762665918456 | recording_1762665799823 | 1,762,665,799,823 | 1,762,665,918,456 | 24 | [{"id":"E4:B3:23:AE:04:EE","position":"left_hub","connectionId":"Eidon-Tracker-04EE"},{"id":"E4:B3:2(...TRUNCATED) | [{"time":43,"deviceData":{"E4:B3:23:AE:04:EE":[-0.6823120117,-0.1035766602,0.3924560547,0.6080932617(...TRUNCATED) |
1761216220377 | recording_1761216217916 | 1,761,216,217,916 | 1,761,216,220,377 | 24 | [{"id":"E4:B3:23:AE:6E:12","position":"chest","connectionId":"Eidon-Tracker-6E12"},{"id":"E4:B3:23:A(...TRUNCATED) | [{"time":42,"deviceData":{"E4:B3:23:AE:6E:12":[-0.3020019531,0.4963378906,-0.7847290039,0.2158203125(...TRUNCATED) |
1762452413413 | recording_1762452313880 | 1,762,452,313,880 | 1,762,452,413,413 | 24 | [{"id":"E4:B3:23:AD:5A:B2","position":"right_hub","connectionId":"Eidon-Tracker-5AB2"},{"id":"E4:B3:(...TRUNCATED) | [{"time":43,"deviceData":{"E4:B3:23:AD:5A:B2":[-0.4288330078,0.1373901367,-0.1588745117,0.8786010742(...TRUNCATED) |
1761816781151 | recording_1761816657172 | 1,761,816,657,172 | 1,761,816,781,151 | 24 | [{"id":"E4:B3:23:AD:5A:B2","position":"right_hub","connectionId":"Unidentified Eidon Tracker"},{"id"(...TRUNCATED) | [{"time":45,"deviceData":{"E4:B3:23:AD:5A:B2":[-0.6403808594,0.2321777344,-0.3300170898,0.6535644531(...TRUNCATED) |
1762666347788 | recording_1762666300038 | 1,762,666,300,038 | 1,762,666,347,788 | 24 | [{"id":"E4:B3:23:AE:04:EE","position":"left_hub","connectionId":"Eidon-Tracker-04EE"},{"id":"E4:B3:2(...TRUNCATED) | [{"time":44,"deviceData":{"E4:B3:23:AE:04:EE":[-0.5520019531,-0.1790161133,0.4971313477,0.6450805664(...TRUNCATED) |
1761713607425 | recording_1761713517384 | 1,761,713,517,384 | 1,761,713,607,425 | 24 | [{"id":"E4:B3:23:AC:75:DA","position":"left_hub","connectionId":"Eidon-Tracker-75DA"},{"id":"E4:B3:2(...TRUNCATED) | [{"time":42,"deviceData":{"E4:B3:23:AD:AA:D6":[-0.537902832,0.03515625,-0.0228271484,0.8419799805],"(...TRUNCATED) |
Robotic Dataset
The dataset comprises 1,000+ hours of multimodal robot manipulation data collected from real-world environments, demonstrating manipulation tasks such as cleaning, laundry folding, and dishwashing performed by different robots. It contains synchronized sensor data from seven 9-axis IMU units attached to the robot arms, forearms, and chest, along with head-mounted videos and detailed trajectory recordings.
By utilizing this dataset, researchers can explore learning methods and robot manipulation techniques that enhance the ability of real robots to handle different objects and perform complex actions in real-world scenarios. - Get the data
Designed for robotic learning and model training, it provides data suitable for training models in robotic systems, foundation models, and real-world applications of humanoid robotics and robot manipulation.
Frequently Asked Questions
What types of household activities are included?
The dataset focuses on everyday household tasks, including cleaning, laundry folding, and dishwashing.
Who can benefit from this robot dataset?
The dataset is useful for robotics researchers, AI developers, universities, embodied AI teams, and companies working on robot learning. It supports projects involving imitation learning, activity recognition, multimodal sensor fusion, and autonomous robotic assistants operating in home environments.
💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.
Dataset enables researchers and developers to build large-scale robot learning models, making it an essential resource for advancing robotic manipulation and real-world robotics research.
🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects
- Downloads last month
- 42