Simulation gets you 70% of the way. The remaining 30% — the compliance constraints, the unpredictable human interactions, the SOP-governed edge cases — only exists in real enterprise deployments. Sirona operates in those environments every day. We capture, structure, and label what happens there.
Enterprise environments impose constraints that no simulation fully captures — LIMS-triggered workflows, GMP-governed handoffs, shift-change protocols, and the precise way a human technician corrects a robot when it makes a mistake.
Accelerate sim-to-real transfer with data captured in real enterprise facilities — not controlled labs. Authentic layouts, SOP-driven workflows, and genuine edge-case recoveries from live deployments.
Vision, voice, and force-torque traces from authentic human-robot collaboration — labelled and ready for policy training across Isaac Sim, PyTorch, HuggingFace, and standard frameworks.
Edge-case recoveries, SOP deviations, and handoff events in human-shared spaces — exactly the scenarios safety evaluations require and simulations cannot replicate at enterprise scale.
Every sensor stream is time-synchronised, calibration-stamped, and tied to the operational context — task, SOP reference, environment, and human-proximity state — in which it was captured.
ROS bags · HDF5 · Custom JSON schemas · Compatible with Isaac Sim, PyTorch, HuggingFace · Secure API delivery or air-gapped media transfer · Full metadata: timestamps, sensor calibration, environment map, SOP reference
Raw sensor data does not train good robots. Labels do. Every annotation is applied by domain experts — people who understand the operational context, the SOP constraints, and the compliance significance of what they are labelling. No crowdsourcing. No unverified automated-only pipelines.
Semantic labels for objects, instruments, containers, and environments across hospitality, QC lab, and industrial settings. Bounding boxes, segmentation masks, and depth-aligned labels for every modality.
Fine-grained action boundaries with intent labels — pick, place, transport, load, inspect, handoff. Labelled at millisecond resolution against task-state logs and synchronised sensor streams.
Every task sequence is labelled against the governing SOP — correct, deviated, recovered, or escalated. Critical for compliance-aware policy training and safety validation pipelines.
Near-miss events, human-proximity incidents, emergency stops, and recovery actions — labelled with cause, context, and resolution. Exactly what safety evaluations and regulatory submissions require.
Sirona's capture infrastructure is deployed across multiple facility types — not a single controlled lab. This gives your training pipeline the environmental diversity it needs to generalise beyond one setting.
Multi-floor hotel environments with real housekeeping and laundry workflows. Narrow corridors, trolley dynamics, linen handling, supply restocking, and guest-area constraints. Capturing in active properties.
Compliance-grade QC lab environments with LIMS integration. Sample handling, instrument loading, reagent management, and hazmat-adjacent workflows operating under GMP and ISO 17025 constraints.
Cobot and AMR environments in manufacturing and logistics settings. Production line integration, MES-connected workflows, shift-based operational patterns, and multi-robot coordination scenarios.
Every dataset pack comes with full chain-of-custody documentation. Data governance is an architectural decision at Sirona — not a checkbox.
We make a curated data snippet available to qualified robot developers and manufacturers before any commercial discussion. Specify your modality, environment type, and task area — and we will prepare a representative extract from our current corpus.
"The right dataset changes what you can build. The wrong one wastes six months of training runs."
— Sirona Data TeamOr reach us directly: [email protected]