TalosHub captures real manufacturing workflows and turns them into structured, machine-state-aligned task packs: multi-view video, phase labels, exception and recovery annotations, schema documentation, sample loaders, and train/val/test splits.
Captured directly from operating manufacturing facilities. Real operators, real machines, real production environments — not laboratory demonstrations.
Robotics is moving from demos to deployment. The open datasets that helped general manipulation research — Open X-Embodiment, Bridge, DROID — are not enough for factory deployment. They usually lack machine-state context, fixture variance, operator workarounds, and recovery behavior. That is the data gap TalosHub fills.
Industrial robot installations reached US$16.7B in global market value as of the most recent IFR data — a record high — and humanoid programs are widely understood to be bottlenecked on real-world manipulation data with task-specific context. The teams that win the next 18 months are the ones with structured, high-density manufacturing data. The teams building it themselves are losing 6–12 months to facility access and operator wrangling. That is the gap TalosHub fills.
Record-high global market value, signaling industrial deployment is accelerating beyond what training data infrastructure can support.
Public datasets cover lab manipulation. They lack machine-state context, fixture variance, operator workarounds, and recovery behavior — the signals that determine whether a policy survives factory deployment.
Teams building manufacturing capture programs in-house lose 6–12 months to facility access negotiations, operator recruitment, capture rig engineering, and annotation workflow setup.
Humanoid programs across the field are widely understood to be bottlenecked on real-world manipulation data with task-specific context — not on compute, not on architecture, but on data.
Source: International Federation of Robotics, World Robotics 2026 trends report.
Most robotics training data comes from controlled labs. Factories are different: CNC doors open and close, chucks clamp and release, alarms fire, chips build up, fixtures drift, operators improvise, and recovery behavior matters. TalosHub captures those real workflows inside active manufacturing facilities and packages them into structured episodes your team can inspect, load, and train against.
Not raw footage. Not generic datasets. Scoped, labeled, segmented, documented, and white-labeled under your brand.
For organizations with existing factory video assets, TalosHub can process source material into the same canonical structured format we deliver from our own captures. Same schema. Same annotations. Same quality gates. Different data source. Common engagement pattern for manufacturers with established operator video archives who want to convert that data into trainable assets.
Task family, episode count, exception coverage, machine families. Defined before capture.
Up to five synchronized cameras per workflow. Three fixed positions (overhead, front, side) plus optional wrist-mounted cameras for fine-motion detail. Synchronized streams with ≤50ms maximum drift.
Live events from CNC controllers via OPC-UA, MTConnect, or FOCAS. Door, chuck, alarms, cycle timing — millisecond-timestamped machine events aligned to video within the episode sync tolerance.
Workflow decomposed into millisecond-precision phases. 100% coverage, validated by inter-rater agreement.
Each exception tagged with severity, recovery action, and success state. Per-task-family taxonomy.
Per-episode quality scores across video, labels, sync, and exceptions. Gates at 0.85 minimum.
Pre-stratified by exception type so each split contains proportional coverage. 70/15/15 default.
Schema reference, taxonomy, capture notes, quality assessment, and a Python loader for PyTorch and TensorFlow.
Pack delivered under your branding for customer-facing artifacts. Internal provenance metadata and licensing terms preserved per agreement.
Every task pack follows the same operating model: scope the workflow, capture synchronized episodes, structure and validate labels, package the dataset, then hand off with a review and gap map. The output is not a folder of video. It is a dataset your team can evaluate immediately.
Define task family, episode count, exception coverage, and machine families. We work with your team to lock the scope before any capture begins.
Up to five synchronized cameras running inside an active manufacturing facility. Millisecond-timestamped machine-state events aligned to video within the episode sync tolerance.
Phase segmentation, exception annotation, behavioral metadata extraction, and inter-rater validation on a sample of episodes.
Canonical episode JSON, train/val/test splits, sample loader, schema documentation, and per-episode quality scores. Quality gates at 0.85 minimum.
White-labeled task pack delivered to your team. Walkthrough session covering schema, loader integration, and a gap map for any future capture.
Three buyer paths. Three different evaluation criteria. Same canonical task pack.
Load the sample manifest, inspect phase labels, validate sync precision, and test the loader before committing to a full pack. Every TalosHub pack ships with the same RLDS-compatible schema and reference loader you can run locally on a sample episode.
Request a sample task pack →Schema, sample manifest, and loader code shared before scoping.
Close the factory-data gap without spending months building capture operations, facility access, and annotation workflows in-house. TalosHub captures inside operating manufacturing facilities and delivers structured task packs in 4 weeks — not the 6 to 12 months an in-house program takes.
See the engagement model →See how a 4-week task pack engagement actually works.
Support a manufacturing pilot with a white-labeled task pack scoped to the customer workflow you need to prove. The data ships under your brand, follows your customer's task definition, and lets your team start training without operational lift on the customer side.
Book a 20-min scoping call →20-minute call. Workflow scoping, not pitch.
Client-commissioned datasets are tenant-isolated and delivered only under the terms of the client agreement. Reference datasets and partner-facility datasets are governed by separate consent, facility, and licensing agreements. Operator consent is recorded for every capture session, and no real operator names are included in client deliverables.
We will respond within 24 hours, share the relevant schema and sample format, and schedule a 20-minute scoping call if there is a fit.
What happens after you reach outConfirmation of scope and any clarifying questions.
Relevant dataset card section, sample manifest, and loader code reference.
Workflow definition, exception priorities, and timeline.
Task pack scope, deliverables, lead time, and commercial terms.