10 MCAP Datasets for Physical AI You Can Explore in 2026

MCAP is presented as a timestamp-indexed, serialization-agnostic container format suited to robotics and physical AI because it preserves asynchronous sensor streams such as cameras, LiDAR, radar, IMUs, GPS, and transforms within continuous recordings rather than forcing them into independent frames. FiftyOne has repackaged 10 robotics datasets, totaling 89 MCAP episodes across urban driving, SLAM, aquatic and forest navigation, UAV mapping, social navigation, and synthetic environments, enabling synchronized exploration in its multimodal viewer. Most datasets provide raw sensor and trajectory data without object annotations, while SemanticSpray++, selected Boreas episodes, and CMHT include embedded 2D or 3D object labels, and NavWareSet contains timestamped pedestrian cuboids. The overview also identifies recurring conversion challenges, including incompatible sensor schemas, faulty timestamps, frame-count mismatches, missing calibration, and placeholder point data, emphasizing that corrections should be derived from source evidence rather than fabricated. Dataset coverage is often partial, sensor configurations and licensing vary substantially, and labels are generally stored as timestamped MCAP channels to remain synchronized with each multi-minute episode.

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