01 // INFRASTRUCTURE
PRODUCTION
INFRASTRUCTURE
MULTI-MODAL SENSOR FUSION
Hardware-level synchronization across vision, proprioception, IMU, audio, and depth. Sub-millisecond timestamp alignment with nanosecond-precision Unix epochs.
Vision1920×1080 @ 30fps
Proprioception75-920Hz JSONL
IMU1000Hz
Sync Precision<1ms
ANNOTATION PIPELINE
Human-verified labels with automated quality checks. Distributed annotation infrastructure with inter-annotator agreement tracking.
QualityHuman-verified
LabelsSuccess/failure
TrackingInter-annotator
ScaleLinear throughput
02 // DATA FORMATS
TECHNICAL
SPECIFICATIONS
SENSOR MODALITIES
RGB VisionH.264
ProprioceptionJSONL
3D PoseNPZ
AudioWAV
DATA FORMATS
TimestampsUnix ns
VideoH.264
Sensor LogsJSONL
MetadataJSON
DELIVERY
CDNGlobal
APIREST
StorageS3
BatchPB-scale
[ ML-READY ][ ZERO PREPROCESSING ][ UNIFIED SCHEMA ]
03 // APPLICATIONS
TRAINING
PIPELINES
POLICY TRAINING
IMITATION LEARNING
Success-labeled trajectories from real robot deployments. Complete state-action pairs with synchronized vision and proprioception. Ready for behavior cloning and inverse RL.
PRE-TRAINING
FOUNDATION MODELS
Large-scale multi-modal data across diverse tasks and robot morphologies. Vision-language-action triplets for generalist policy pre-training.
MOTION CAPTURE
HUMAN-ROBOT INTERACTION
Multi-perspective human motion with 3D pose annotations. First-person and external viewpoints synchronized with body landmark tracking.
CONTINUOUS LEARNING
PRODUCTION DEPLOYMENT
Real-world failure modes and edge cases from live deployments. Continuous data collection for online learning and policy updates.
SCALE YOUR
TRAINING PIPELINE
Start with sample datasets to validate your approach. Scale to petabyte-batch production with custom collection infrastructure.