Data as of Aug 25, 2026 · Based on 38 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
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Source | Citation share |
|---|---|
Robot Training Data Infrastructure | Axis Roboticshttps://axisrobotics.ai/product/robot-training-data-infrastructure | 2.6% |
 Foxglove: Agentic data platform for Physical AIhttps://foxglove.dev/ | 2.6% |
Robotics AI Infrastructure Platform | FireLoophttps://www.fireloop.ai/ | 2.2% |
 Training Data for Physical AI - Gerrahttps://www.gerra.com/robotics | 2.2% |
Knonik — The Canonical Data Layer for Physical Intelligencehttps://www.knonik.com/ | 2.2% |
Bringup Labshttps://bringup.dev/ | 1.8% |
Mosaico | Next-Gen Roboticshttps://mosaico.dev/ | 1.8% |
 50 startups transforming industries with physical AIhttps://www.bvp.com/atlas/50-startups-transforming-industries-with-physical-ai | 1.8% |
Cerebrichttps://www.cerebric.dev/ | 1.8% |
Neuracorehttps://www.neuracore.com/ | 1.8% |
Startups providing data infrastructure for training robotics models across fleets include specialized physical AI data layers like Encord (Anchor), Dynamic Intelligence, XDOF, and Formant . Because training "Physical AI" requires multimodal data (video, proprioception, and force-torque) rather than just text or images, a new stack of infrastructure startups has emerged to help companies orchestrate data collection, curation, and continuous fleet learning.
The leading startups in this category are organized below by their specific role in the robotics data infrastructure pipeline.
End-to-End Robotics Data Layers
Fleet Telemetry & Devops Infrastructure
Trajectory Curation & Training Platforms
Crowdsourced & Managed Real-World Data Gathering
If you want to narrow this down, let me know:
Yes. If by “data infrastructure for training robotics models across fleets” you mean the layer that collects robot trajectories, synchronizes/cleans them, curates datasets, and feeds model-training/evaluation loops across many deployed robots, the market is emerging quickly.
I'd split the market into three layers:
| Layer | Companies | What they provide |
|---|---|---|
| Fleet data infrastructure | Foxglove, potentially Encord | Ingest, store, search, visualize, label, curate and evaluate robot data |
| Robot-native data collection | XDOF, Telemanual, Scale AI, Robgence, Dexset | Teleoperation, demonstrations, sensor/video capture, annotation |
| Synthetic / scalable data | Axis Robotics and simulation providers | Generate trajectories without physically operating every robot |
There's also a strategically important fourth category: robotics companies building their own data flywheels. Skild AI is a good example: it explicitly describes using data from different robot embodiments and deployments to improve its model, creating a loop where more deployed robots generate more training data.
I'd pay particular attention to Foxglove and XDOF.
Foxglove is closer to the software infrastructure/control plane: What happened across our fleet? Find the failures, inspect them, curate them, and turn them into training/evaluation data.
XDOF is closer to the data supply chain: How do we actually generate enough high-quality physical interaction data, clean it, annotate it, and continuously feed models?
The really valuable company may ultimately sit between those two: a system of record for every robot trajectory + automated failure mining + dataset/version management + evaluation + a closed-loop mechanism that tells a fleet what data to collect next.
If you're evaluating this market for investment, competitive intelligence, or starting a company, I can also map ~20 startups by funding, customers, data modality, fleet size, moat, and where they sit in the robotics data stack.
Several modern startups explicitly focus on providing the data infrastructure and lifecycle tooling required to ingest, manage, curate, and utilize physical AI data across robotic fleets:
Dedicated Robotics Data Infrastructure & Analytics
Distributed Data Capture & Teleoperation Platforms
Open-Source Coordination Layers
Are you looking for infrastructure tools to stream and query telemetry/logs from already deployed hardware , or do you need a platform to outsource and label net-new training data?
Startups in this space are building the “data layer” for physical AI: systems that collect, clean, label, simulate, store, and distribute robot experience so models can be trained across many robots and deployments rather than one lab robot at a time. The market is still forming, but several notable companies fit this category:
| Startup | What they provide | Why it matters |
|---|---|---|
| Scale AI (robotics initiatives) | Data labeling, evaluation, and AI data infrastructure extended toward robotics | Brings mature AI data operations to embodied AI workflows |
| Sensei Robotics | Human demonstration collection infrastructure; a network of operators generating robot training data | Similar to “Scale AI for robotics data”; focuses on scalable teleoperation and demonstrations Y Combinator |
| Robgence | Egocentric video, multimodal sensors, motion capture, teleoperation datasets, synthetic data | Provides datasets aimed at humanoid and embodied AI models Robgence |
| Asimov | Real-world human movement data collection for humanoid robots | Targets the missing diversity problem: robots need data from many environments and behaviors, not only factories Y Combinator |
| Gerra | Own robot fleets producing multimodal sensor streams, teleoperation data, and demonstrations | Focuses on owning the data-generation pipeline rather than aggregating datasets gerra gerra |
| Axis Robotics | Simulation-based training data generation, trajectory collection, sim-to-real pipelines | Aims to produce large-scale robot trajectories without requiring equivalent physical robot hours AXIS ROBOTICS AXIS ROBOTICS |
| SimMint | Synthetic robot data factories, calibrated simulations, labeled trajectories | Focuses on generating site-specific synthetic data for industrial robots SimMint |
| Khenda Robotics | Converts factory video into structured robot training data | Targets the large amount of unused operational video already generated on factory floors Khenda |
| FireLoop | End-to-end robotics workflows: data collection, simulation, training, evaluation | Positions itself as infrastructure connecting the robotics ML lifecycle FireLoop |
These companies solve: “How do we get millions of robot experiences?”
Examples:
Typical data:
Y Combinator Robgence gerra### 2. Simulation and synthetic data factories
These solve: “How do we generate enough edge cases?”
Examples:
Typical outputs:
AXIS ROBOTICS SimMint### 3. Robot data management and model-development infrastructure
These solve: “How do robotics teams turn raw experience into training pipelines?”
Examples:
Capabilities:
Khenda FireLoop## Companies to watch from an investor / ecosystem perspective
The most strategically interesting businesses may be those building a data network effect:
The core bottleneck is shifting from “can we build a robot?” to “can we acquire enough diverse physical-world experience to train a general robot model.”
If you are looking specifically for venture-backed startups in this category (Seed–Series B) or a competitive landscape vs. Scale AI / NVIDIA / Tesla robotics data stacks, the list changes quite a bit.
Yes. There’s a fast-growing layer of startups focused specifically on the data flywheel for physical AI—collecting robot/fleet data, indexing it, curating trajectories, labeling failures, and feeding it back into training.
| Startup | What it provides | Fleet / training angle |
|---|---|---|
| Cerebric | End-to-end physical-AI data infrastructure | Captures synchronized multimodal data from an entire robot fleet, stores/version-controls it, enables petabyte-scale search, then provides managed training/inference. www.cerebric.dev |
| Foxglove | Robotics observability + multimodal data platform | Strongest fit for robots already deployed in the field: ingest fleet logs, search across missions, curate datasets and use them to train new models. foxglove.dev |
| XDOF | Robot-data collection, pipelines, annotation | Builds the collection/data/annotation layer for robotics-model developers. Raised $70M and reported ~20 customers in 2026. techcrunch.com |
| Sensei Robotics | Human-demonstration data collection | Hardware + software + a distributed operator network designed to generate robot-training trajectories at scale—essentially a “Scale AI for robotics data.” www.ycombinator.comtechcrunch.com |
| Luckey | Egocentric human/work data | Captures real work from workers across factories, warehouses, kitchens, etc.; says it has 20,000+ collectors across five continents, turning that data into robotics training datasets. luckey.to |
| Khenda Robotics | Video → structured robot-training data | Converts ordinary factory video into discrete actions and ML-ready datasets, aiming to unlock existing operational video as training data. www.khendarobotics.com |
| Axis Robotics | Simulation + crowdsourced robot trajectories | Browser-based simulation/teleoperation and community data collection; reports nearly 100K trajectories generated in five days and a sim-to-real training loop. axisrobotics.ai |
| Bringup Labs | Fleet data/ops infrastructure | Ingests and indexes multimodal robot logs across a fleet, with experiment tracking, visualization, simulation, deployment and fleet updates. bringup.dev |
| FireLoop | Closed-loop robotics AI infrastructure | Connects data collection, simulation, training and evaluation into a continuous learning loop. www.fireloop.ai |
There are really three different businesses hiding under “robotics data infrastructure”:
1. Fleet data infrastructure — “Datadog/Snowflake for robots”
Cerebric, Foxglove and Bringup are closest to this. Their moat is the connection to deployed fleets: ingesting sensor streams, indexing missions, finding failures, and turning those failures into training/evaluation data.
2. Training-data supply — “Scale AI for physical AI”
XDOF, Sensei and Luckey are more about creating the missing data. Humans teleoperate robots, demonstrate tasks, or wear cameras while doing real work. This is particularly important because unlike LLMs, robotics doesn't have an enormous existing corpus of naturally occurring training data.
3. Synthetic/community data engines
Axis is an interesting example: rather than deploying expensive physical robots everywhere, it uses browser-based simulation and a large contributor network to generate trajectories, then closes the sim-to-real loop.
If by “across fleets” you mean “a startup that sits between hundreds/thousands of deployed robots and the model-training stack, continuously turning fleet experience into better foundation models,” I'd focus most closely on:
Cerebric → Foxglove → Bringup → XDOF
Cerebric is probably the most direct match to your wording because it explicitly describes the workflow as capture from the entire fleet → store/version → search/analyze → train → deploy, i.e. a physical-AI data flywheel.
The broader thesis is gaining traction quickly: the bottleneck is shifting from simply building robot models to building the data engine that continuously improves those models from real-world experience. Recent startups are attacking different pieces of that loop rather than competing purely on model architecture.
If you're evaluating this as a startup/investment landscape, I can also map these companies by funding, founders, customers, estimated traction, technical moat, and which layer of the robotics data stack they own.
The bottleneck in scaling physical AI and Vision-Language-Action (VLA) models has shifted from raw compute to multimodal data infrastructure and fleet-wide learning pipelines . A rising wave of specialized startups is tackling the ingestion, synchronization, annotation, and closed-loop training challenges across distributed robot and vehicle fleets.
The primary startups and platforms providing data infrastructure for training robotics models across fleets include:
If you are working on a specific architecture, let me know:
I can help narrow down which tool fits your stack best.
Several specialized startups and platforms provide AI-native data infrastructure, curation, and closed-loop learning pipelines explicitly tailored for training robotics and Vision-Language-Action (VLA) models across distributed hardware fleets.
Specialized Robotics Data & Observability Platforms
Broader Physical AI & Fleet Infrastructure
If you're working with a specific type of robot—such as humanoid manipulators, industrial AGVs/AMRs, or drones —let me know so I can tailor these platform capabilities to your sensor and telemetry requirements.
Yes. If by “data infrastructure for training robotics models across fleets” you mean the layer that captures robot experience, stores/indexes multimodal logs, curates trajectories, feeds training, and closes the loop back into deployed fleets, there’s a rapidly forming startup category around this.
| Startup | What it does | Fleet → training loop? | My take |
|---|---|---|---|
| Foxglove | Multimodal robot data platform: ingest, search, visualize, curate, evaluate and manage fleet data | Yes | Most established / broadest infrastructure play |
| Cerebric | Captures synchronized multimodal data across fleets, petabyte-scale research, managed training/inference | Yes | Very close to the “robot data cloud” thesis |
| Proxy Robotics | Teleoperation + data engine + fleet operations; converts deployment episodes into training datasets | Yes | Particularly interesting for the deployment → data → autonomy flywheel |
| Neuracore | Teleop collection, dataset versioning, imitation/RL training and deployment monitoring | Yes | More of a robotics ML platform than pure data infrastructure |
| Bringup Labs | Fleet data, experiment tracking, pipelines, remote access, testing and fleet updates | Yes | Interesting “Datadog + CI/CD + data layer for robots” approach |
| Voxel51 / FiftyOne | Multimodal dataset search, curation, annotation and evaluation for perception/VLA models | Partially | Excellent data/ML layer, less fleet-operations-centric |
| Axis Robotics | Distributed simulation and synthetic trajectory generation | Indirectly | More focused on creating training data than managing deployed-fleet data |
| Gerra | Operates robot fleets to generate multimodal training data for Physical AI | Yes, but as a data provider | Interesting if you're looking for outsourced data rather than infrastructure |
| Robgence | Human/robot data collection, annotation, teleoperation and dataset delivery | Partially | More “Scale AI for robotics” than software infrastructure |
Foxglove is probably the clearest example of the “data infrastructure layer for a fleet of robots” thesis. Its current platform explicitly covers fleet capture, a searchable system of record, multimodal data management, visualization, and dataset curation for training.
It has also moved well beyond visualization: in 2025 it raised a $40M Series B specifically to expand its Physical AI data platform, including petabyte-scale storage/search and the broader development → testing → operations lifecycle.
The architectural idea is essentially:
robot fleet → logs/demonstrations → centralized multimodal data layer → failure/edge-case mining → training dataset → new model → fleet deployment → more data
That's a very compelling infrastructure position because the data becomes a proprietary feedback loop rather than merely a storage problem.
I would separate these companies into three layers:
1. Fleet data infrastructure
These are closest to “Snowflake/Datadog for physical AI.”
2. Robot ML / training infrastructure
These sit closer to the model-development workflow: dataset curation → training → evaluation → deployment.
3. Data acquisition / data generation
These solve the harder question of where the training data comes from—teleoperation, real-world deployments, human operators, or simulation.
Proxy is especially interesting because it explicitly connects managed human intervention, fleet operations, and its “Forge” data engine: real-world robot episodes become versioned training datasets that feed back into the autonomy stack.
And Cerebric is perhaps the most direct articulation of the fleet → foundation-model training thesis: it says it captures synchronized multimodal data from an entire fleet, stores it in the cloud, makes missions searchable at petabyte scale, and exports scenarios directly as training data.
If you're researching this as an investment/startup category, I'd focus particularly on Foxglove, Cerebric, Proxy, and Neuracore. They represent four slightly different bets on who owns the robotics data flywheel.
The physical AI and robotics boom has shifted the primary bottleneck from hardware and model architecture to multimodal data infrastructure . Training general-purpose Vision-Language-Action (VLA) models and robot policies requires capturing, cleaning, and synchronizing complex physical signals (RGB-D video, joint telemetry, tactile force, and IMU data) across distributed deployments.
Several notable startups and specialized infrastructure providers focus on solving this data and fleet-level learning challenge:
Would you like to explore how these startups contrast teleoperation vs. human-sensor data collection , or are you looking for infrastructure tailored to a specific hardware stack?
Yes. If by “data infrastructure for training robotics models across fleets” you mean the layer that turns streams of robot/vehicle sensor data into searchable, curated, training-ready datasets—and ideally closes the loop back into deployment—the startup landscape is getting interesting.
| Startup | What it does | Fleet/data angle |
|---|---|---|
| Foxglove | Multimodal observability + data platform for robotics | Probably the clearest fit: captures data from fleets, lets teams search across logs, curate datasets, and use them for model training. www.foxglove.dev |
| Continuum | Replayable data infrastructure for autonomous machines | Builds a system of record for robot runs/sensor events, with query + replay so teams can correct data and retrain from historical runs. Explicitly targets humanoid labs, AVs, drones and robot fleets. continuum.moralis.com |
| Avala | Managed “data engine” for embodied AI | Particularly interesting if you want the closed-loop training piece: ingest fleet data, annotate/manage datasets, train VLA/driving/robotics models, and continuously improve them. avala.ai |
| Neuracore | End-to-end robot data → policy → deployment stack | Collects teleop demonstrations, versions datasets, trains policies, deploys them and monitors real robots. Explicitly keeps data, policies and fleets connected. www.neuracore.com |
| Mosaico | Open-source robotics data infrastructure | More of a foundational data layer: ingestion, ontology, querying and petabyte-scale sensor-data management, including querying events across recordings. mosaico.devwww.knonik.comwww.gerra.com |
| Knonik | Data plumbing for physical AI | Ingests heterogeneous robot formats/live streams, compresses them, scores data quality, annotates episodes and feeds training pipelines. www.knonik.com |
These are less “Databricks for robot fleets” and more suppliers of the data that the infrastructure consumes:
There are really three layers emerging:
1. Fleet data infrastructure
Foxglove, Continuum, Mosaico, Knonik, Avala
→ “Give me all the robot experience and make it usable.”
2. Data-generation networks
XDOF, Sensei, Gerra, Robgence, Cervo
→ “Generate the missing physical-world experience.”
3. Fleet-learning / policy layer
Neuracore and ultimately the robotics foundation-model companies
→ “Turn accumulated experience into a better policy and push it back to the fleet.”
That third loop is arguably the most strategically interesting. Recent research is demonstrating that a shared policy can improve as experience and human interventions accumulate across a heterogeneous fleet, rather than treating each robot's dataset as an isolated training set.
If I were mapping the startup opportunity specifically around “Snowflake/Databricks for robot fleets,” my shortlist would be Foxglove, Continuum, Avala, Mosaico, and Knonik. Foxglove is the most established-looking fleet observability/data product; Continuum and Avala are especially interesting around the replay → retrain → redeploy data loop.
