Robotics Training Data Statistics (2026): 50+ Data Points on Dataset Sizes, Demonstration Costs, and Scaling Laws

The largest disclosed training set behind any robot foundation model — Physical Intelligence's π0 — holds just over 10,000 hours of robot data, and Goldman Sachs estimates leading humanoid programs have accumulated roughly 500,000 hours. Set that against the 15 trillion tokens of text behind Llama 3, and the defining constraint of embodied AI in 2026 is obvious: robot demonstrations are the scarcest input in AI. Figure trained a full humanoid upper-body controller on ~500 hours; NVIDIA pretrained GR00T N1 on 88 real hours stretched tenfold with synthetic data. We aggregated data from arXiv papers (Open X-Embodiment, DROID, π0, GR00T N1), official reports from NVIDIA, Figure, Google DeepMind and Physical Intelligence, market research from Mordor Intelligence and Grand View Research, and dozens of other primary sources — 56 verified statistics in total.

01The Data Bottleneck: Robot Demonstrations vs Internet-Scale Text

Language models read the internet; robots cannot. Every robot demonstration has to be produced physically — by a teleoperator, a suited human, or a simulator — so the biggest robot datasets are measured in thousands of hours while text corpora are measured in trillions of tokens. As Figure puts it, you cannot scrape the internet for robot data.

Note: Open X-Embodiment is published in trajectories, not hours; the ~2,000-hour estimate comes from AgiBot World's published comparison.

02Training Data Behind Major Robot Foundation Models

Disclosed training corpora span two orders of magnitude — from GR00T N1's 88 real hours to π0's 10,000+. The spread shows there is no settled recipe yet: Helix bets on data efficiency, π0 on volume, Gemini Robotics on a year of fleet-scale ALOHA 2 collection. For model capability numbers behind these datasets, see our vision-language-action model statistics.

Note: RT-1 figures (2022) are included as the historical baseline that Open X-Embodiment and later VLA models built on.

03The Open Dataset Landscape

The public corpus changed in composition, not just size: 2024–2026 additions skew toward humanoid, bimanual, and egocentric-video data rather than more single-arm pick-and-place. Failure data now ships deliberately — RoboMIND includes 5,000 labeled failure demonstrations. Open sets remain research-licensed in several cases, which is why commercial teams pair them with in-house collection or purpose-built real-world data production.

04What Demonstration Data Costs to Collect

The honest unit economics of teleoperation: skilled human time, robot fleet time, and calendar months. DROID's 350 hours consumed 50 collectors for a year; Tesla pays up to $48/hour for suited data operators. The counterweight is fine-tuning efficiency — 15 minutes to an hour of demonstrations now adapts a pretrained generalist to a new task. We break the collection side down further in our teleoperation and demonstration data statistics, and the same spec → pilot → scale logic drives how production data engagements are typically structured.

Note: per-hour data prices published on SEO blogs could not be traced to any primary source and were excluded from this article.

05Real, Synthetic, and Human-Video Data Mix

2025–2026 pipelines treat real teleoperation as the scarce top of a pyramid and multiply it: GR00T N1 stretched 88 real hours to 827 with generated neural trajectories, and DexMimicGen minted nine months of demonstration-equivalents in 11 hours. The other frontier is human video — Apple's EgoDex and Figure's Brookfield partnership turn people, not robots, into the sensor, using head-mounted and stereo egocentric capture hardware instead of robot fleets.

Note: Figure reports its humanoid learned end-to-end navigation from egocentric human video alone — no robot-specific training data.

06Data Scaling Laws for Robot Policies

The field's most useful 2024–2025 finding: generalization scales as a power law with the diversity of environments and objects, not raw demonstration count. Past a per-environment threshold, more demos of the same thing buy almost nothing — which redirects data budgets from volume to variety.

Note: GR00T N1 also beat a Diffusion Policy baseline by 32.4% in the 10%-data setting — pretraining compounds the value of every collected hour (NVIDIA, GR00T N1 2025).

07The Robot Data Market

Two research firms scope the money differently but agree on direction: roughly 3x–5x growth in data collection and labeling spend by the early 2030s, with synthetic generation and sensor-fusion streams growing fastest. Humanoid demand is the pull — Goldman Sachs models $38B in humanoid revenue by 2035, Morgan Stanley $5T by 2050.

Note: Mordor and Grand View scope the market differently (labeling services vs training datasets), so the figures bracket the spend rather than conflict.

Summary: Robotics Training Data by the Numbers

Methodology and Sources

This article aggregates 56 statistics from 26 primary sources: peer-reviewed and preprint research on arXiv, official company publications from NVIDIA, Figure, Physical Intelligence, Google DeepMind, Meta and Apple, market research from Mordor Intelligence and Grand View Research, and financial-press reporting of named analyst estimates. We cite the original study in every case, never a blog quoting it. Statistics that could not be traced to a primary source — including per-hour data prices circulating on AI-generated SEO sites — were excluded. The Tesla pay figure comes from a 2024 job listing that is no longer live; it is corroborated by multiple independent reports and flagged accordingly. Where two research firms size the data market differently, both figures are shown with their scope.

• Meta AI — Introducing Meta Llama 3 (2024)
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models (arXiv 2310.08864, 2023)
• Open X-Embodiment project site (2023)
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset (arXiv 2403.12945, 2024)
• BridgeData V2 (arXiv 2308.12952, 2023)
• π0: A Vision-Language-Action Flow Model (arXiv 2410.24164, 2024)
• π0.5: A VLA with Open-World Generalization (arXiv 2504.16054, 2025)
• Figure — Helix announcement (2025)
• Figure — Project Go-Big (2025)
• NVIDIA — GR00T N1 paper (arXiv 2503.14734, 2025)
• NVIDIA Newsroom — GR00T N1.5 / GR00T-Dreams (2025)
• NVIDIA — Cosmos world foundation models (2025)
• NVIDIA Newsroom — Cosmos platform launch (2025)
• Google DeepMind — Gemini Robotics (arXiv 2503.20020, 2025)
• Google Research — RT-1 (2022)
• Google DeepMind — RT-2 announcement (2023)
• OpenVLA (arXiv 2406.09246, 2024)
• Data Scaling Laws in Imitation Learning for Robotic Manipulation (arXiv 2410.18647, 2024)
• Mobile ALOHA (arXiv 2401.02117, 2024)
• AgiBot World dataset cards (2025)
• AgiBot World Colosseo project page (2025)
• RoboMIND (arXiv 2412.13877, 2024)
• RoboMIND 2.0 (arXiv 2512.24653, 2026)
• Ego4D (arXiv 2110.07058, 2022)
• Apple — EgoDex (arXiv 2505.11709, 2025)
• Mordor Intelligence — Data Collection & Labelling Market (2026)
• Grand View Research — AI Training Dataset Market (2026)
• Goldman Sachs Research — humanoid robot forecast (2024)
• Morgan Stanley Research — The Humanoid 100 (2025)
• Bloomberg — China robot-data reporting (July 2026)
• IoT World Today — Tesla data-operator listing (2024)

Last updated July 2026. We update this page quarterly as new data becomes available.