Akaispacelabs2026apache-2.0
Akai_Ego_100_Hrs_v1
A collection of approximately 100 hours of egocentric recordings captured across diverse real-world activities and environments, with synchronized RGB video and inertial measurements for embodied AI and multimodal learning research.
Downloads32
Hours100
Likes1
Why This Matters for Physical AI
This egocentric dataset with synchronized video and IMU data enables training of embodied foundation models and vision-language-action systems that can learn human movement and interaction patterns from real-world experiences.
Technical Profile
- Modalities
- rgbimu
- Environment
- indoor
- Total Hours
- 100
- Data Format
- mp4, csv, json, parquet
- Annotation Types
- metadata
- License
- apache-2.0
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