Stera-10M: 200 Hours of Egocentric Multimodal Sensor Data

Stera-10M is an open egocentric multimodal dataset for embodied AI, robotics, world models, and spatial intelligence. It contains 200 hours of synchronized first-person recordings across 500+ sessions from 20 contributors in 20+ unique environments, with 10 million RGB frames, LiDAR depth, and ARKit data. The dataset was captured end-to-end on commodity iPhone Pro hardware through the open Stera platform and is authored by fpvlabs.

Use Cases

• Training world models for robotics based on synchronized first-person RGB and LiDAR depth data.
• Developing spatial intelligence algorithms using the dataset's egocentric recordings from 20+ unique environments.
• Benchmarking embodied AI agents on tasks requiring multimodal sensor fusion from commodity hardware.
• Researching human-environment interaction patterns from 500+ sessions of first-person activity.

Strengths

• Contains 200 hours of synchronized first-person recordings.
• Includes 10 million RGB frames alongside LiDAR depth and ARKit data.
• Captured across 500+ sessions from 20 contributors in 20+ unique environments.
• Recorded end-to-end on commodity iPhone Pro hardware, suggesting real-world applicability.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last updated 2026-05-15 14:58:39; freshness should be verified.

Provenance

Source

fpvlabs via the open Stera platform.

Collection Method

Captured end-to-end on commodity iPhone Pro hardware.

Freshness

Last updated 2026-05-15 14:58:39.