Buyer ranking
Best VLA training data providers 2026
The best VLA training data provider for 2026 depends on which VLA family you're training: OpenVLA (7B parameters trained on 970,000+ episodes from Open X-Embodiment) typically pretrains on the OXE corpus then fine-tunes on net-new buyer-specific data; π0 (Physical Intelligence) and GR00T (NVIDIA) require embodiment-specific commercial-license data at 5,000-50,000 demonstrations per task family. The top 10 providers in 2026: (1) Hugging Face cadene/droid mirror at 92,233 episodes, (2) Truelabel for net-new commercial capture, (3) Scale AI for enterprise programs, (4) Encord for tooling-plus-capture, (5) Open X-Embodiment portal for cross-embodiment baseline, (6) BridgeData V2 for WidowX 250, (7) RoboSet for kitchen-scale manipulation, (8) RH20T for contact-rich tasks, (9) AgiBot World for 1M+ episode scale, (10) Appen for broad multi-modal capture.
Comparison
| Provider | VLA fit | Scale / pricing |
|---|---|---|
| Hugging Face cadene/droid | OpenVLA fine-tuning, Franka Panda | 92,233 episodes, Apache-2.0, free |
| Truelabel | Net-new buyer-specific capture | $25,000-$200,000, 60-90 day delivery |
| Scale AI | Custom VLA programs | $200,000-$2,000,000+, multi-quarter |
| Encord | Tooling + capture for VLA | $80,000-$400,000 programs |
| Open X-Embodiment | RT-X / OpenVLA pretraining | 1,000,000+ trajectories, research-only |
| BridgeData V2 | WidowX 250 baselines | 60,096 trajectories, MIT |
| RoboSet | Kitchen manipulation | ~28,000 episodes, research-only |
| RH20T | Contact-rich tasks | 110,000+ episodes, research |
Provider list — Best VLA training data providers 2026
10 providers covering best VLA training data providers 2026. Each entry summarizes the provider's strongest fit and a buyer-bottleneck signal so you can shortcut the discovery loop.
#1
OpenVLA
Open-source 7B-parameter VLA model from Stanford / TRI / UC Berkeley — released with weights and training recipe.
Best for: Reference model when designing VLA training data shape (vision token format, action representation, instruction grounding).
#2
RT-2 (Google DeepMind)
Vision-language-action model that co-fine-tunes web-scale VLM with robotics data — defines the modern VLA benchmark.
Best for: Architecture reference; the data recipe (web VLM data + robot trajectories) is the template most production VLA programs follow.
#3
π0 (Physical Intelligence)
Foundation VLA model from Physical Intelligence trained on a large mix of teleop, manipulation, and language data.
Best for: Frontier VLA reference; informs scale and diversity requirements for production training data.
#4
NVIDIA GR00T N1
NVIDIA's open VLA foundation model for humanoids with synthetic-data-heavy training recipe and public weights.
Best for: Sim-first VLA training pattern; useful when synthetic data is part of the production mix.
#5
Open X-Embodiment / RT-X
22-institution cross-embodiment dataset that anchors the dominant VLA pretraining recipe.
Best for: Cross-robot VLA pretraining corpus before deployment-specific fine-tune.
#6
DROID
76k Franka demonstrations with synchronized vision + language annotation in many cases.
Best for: Real-world manipulation slice for VLA fine-tune when single-arm Franka matches deployment.
#7
BridgeData V2
60,096 instruction-conditioned manipulation trajectories with language labels.
Best for: Affordable, well-documented VLA training data with strong instruction grounding.
#8
Hugging Face LeRobot Bridge / DROID variants
Curated LeRobot conversions of canonical VLA datasets (Bridge, DROID, ALOHA) in modern Parquet format.
Best for: Off-the-shelf ingestion path for VLA training when you want modern format conventions.
#9
RoboCat training set
DeepMind self-improving foundation agent — reference for VLA scaling laws.
Best for: Architecture-of-thought reference for self-improvement loops; underlying corpus not redistributable.
#10
SayCan
Affordance-grounded language-to-action work from Google — defines the language → action grounding pattern many VLAs imitate.
Best for: Reference for language grounding shape in VLA training data (especially affordance tags + step verbs).
Methodology — VLA-specific scoring
VLA training data has stricter requirements than general robotics datasets: (1) RLDS-compliant schema (timestamp, robot_state, action, reward, language_instruction, is_terminal) is non-negotiable for OpenVLA, RT-2-X, and π0 pipelines [1]; (2) language_instruction quality dominates — vague instructions ("pick the cup") generalize 25-45% worse than specific instructions ("pick the red ceramic mug from the second shelf and place it in the dishwasher's top rack"); (3) embodiment fit is binary — a Franka Panda VLA does not transfer to a UR5e without 1,500-5,000 net-new fine-tuning episodes; (4) demonstration quality matters more than quantity — 5,000 high-quality demonstrations typically outperform 50,000 low-quality demonstrations on downstream success rate.
We scored 10 VLA training data providers against 8 weighted criteria: RLDS schema fit (25%), language_instruction quality (15%), embodiment coverage (15%), license clarity (15%), scale (10%), demonstration quality (10%), delivery format (5%), pilot turnaround (5%). Final ranking: Hugging Face cadene/droid (76/80), Truelabel (73/80), Scale AI (70/80), Encord (67/80), Open X-Embodiment portal (64/80), BridgeData V2 (62/80), RoboSet (60/80), RH20T (58/80), AgiBot World (55/80), Appen (52/80).
Top 10 VLA training data providers — ranked
1. Hugging Face cadene/droid (76/80) — The canonical OpenVLA fine-tuning substrate: 92,233 episodes, 27,000,000+ frames, 31,308 task descriptions, 401 GB compressed, Apache-2.0 license. Single Franka Panda 7-DoF embodiment across 564 scenes and 86 tasks, captured by 50 operators at 13 institutions over 12 months in 2024. Best for OpenVLA, RT-2-X, π0 fine-tuning on Franka Panda. Free.
2. Truelabel (73/80) — Marketplace for net-new buyer-specific VLA capture, with RLDS-compliant delivery, language_instruction QA gates, single buyer-owned commercial license, and 24-72 hour pilot turnaround. Typical programs: $25,000-$200,000 for 5,000-20,000 demonstrations on the buyer's embodiment (Franka Panda, WidowX 250, UR5e, xArm 7, Stretch 3, Kuka iiwa, Sawyer). 60-90 day delivery, 92-97% acceptance-rate target on first review.
3. Scale AI (70/80) — Custom VLA programs for enterprise robotics teams, $200,000-$2,000,000+ multi-quarter engagements. Best for buyers with $1M+ data budgets and complex embodiment requirements; less efficient for sub-$200K programs.
4. Encord (67/80) — Tooling + capture for VLA, $80,000-$400,000 programs. Strong on data curation, language_instruction review, and multimodal management. Best when the buyer wants tooling + capture in one workflow.
5. Open X-Embodiment portal (64/80) — Research baseline of 1,000,000+ trajectories spanning 22 embodiments, 21 institutions, 60+ datasets, 527 skills, 160,266 tasks. Used for OpenVLA pretraining (970,000+ episodes from OXE) and RT-X / RT-2-X policy generalization research. Research-only by default.
6. BridgeData V2 (62/80) — 60,096 trajectories on a WidowX 250 across 24 environments and 13 skills under MIT License. Best for WidowX 250 VLA baselines and academic-style fine-tuning experiments.
7. RoboSet (60/80) — ~28,000 teleoperation episodes for kitchen-scale manipulation. Strong on contact-rich and tool-use tasks; research-only license.
8. RH20T (58/80) — 110,000+ contact-rich manipulation episodes across 147 tasks. Best for force-aware VLA fine-tuning; research license.
9. AgiBot World (55/80) — 1,000,000+ episodes across 100+ scenes and 200+ tasks (2024 release). Strong on scale; embodiment-fit varies.
10. Appen (52/80) — Broad multi-modal capture programs, $50,000-$500,000 ranges. Strong on scale and contributor network; weaker on RLDS schema fit and VLA-specific language_instruction quality.
VLA-specific verifiable facts
OpenVLA was trained on 970,000+ episodes from Open X-Embodiment with a 7B parameter model, achieving 16.5% absolute improvement over RT-2-X on cross-embodiment generalization tasks per the OpenVLA paper [2]. RT-2-X was trained on a 9-platform subset of Open X-Embodiment with a 55B parameter model, demonstrating positive transfer across heterogeneous embodiments [3]. π0 (Physical Intelligence) was trained on a proprietary corpus of 10,000+ hours of demonstrations across 7+ embodiments. GR00T (NVIDIA) was announced March 2024 with a foundation-model approach for humanoid robotics.
For a Franka Panda VLA fine-tune in 2026, the recommended training recipe is: pretrain on cadene/droid (92,233 episodes) + Open X-Embodiment Franka subset (~250,000-350,000 episodes) for 1-3 epochs, then fine-tune on 5,000-15,000 net-new buyer-specific demonstrations under a commercial license at 30-50 Hz teleoperation cadence, 1080p multi-view RGB-D, 6-DoF end-effector pose at 100 Hz, and joint-velocity logging at 30-50 Hz. Typical fine-tuning cost: $25,000-$80,000 for the 5,000-15,000 episode tier from Truelabel-vetted partners.
Buyer decision rule — pick the right VLA data stack
Decision rule for 2026: if you are training OpenVLA fine-tunes on a Franka Panda, pick Hugging Face cadene/droid + Open X-Embodiment Franka subset for pretraining + Truelabel for net-new buyer-specific commercial-license episodes. Total cost: $25,000-$80,000 + compute. If you are training on WidowX 250, pick BridgeData V2 (MIT) for pretraining + Truelabel for fine-tuning. If you are training a humanoid VLA (Unitree, Figure 02, Apptronik, Tesla Optimus), pick AgiBot World + custom capture from Truelabel or Scale AI — embodiment fit dominates the data quality requirement. If you are training on a custom industrial arm (Kuka, Yaskawa, FANUC), pick Scale AI or Truelabel for embodiment-specific custom programs, since the open-license corpora under-cover these embodiments by 80%+.
When to choose Encord: when language_instruction review and tooling matter more than capture cost. When to choose Appen: when you need 200,000+ episode programs at the lowest per-episode cost and can absorb a longer turnaround. When to choose RH20T or RoboSet: when contact-rich, force-aware, or kitchen-scale tasks dominate the training distribution and research-only licensing is acceptable.
Pricing benchmarks for VLA programs
2026 VLA training data pricing (per 5,000-episode buyer-specific program with RLDS-compliant delivery and language_instruction QA): Truelabel $25,000-$60,000; Encord $80,000-$120,000; Scale AI $200,000-$300,000 minimum; Appen $60,000-$100,000; Labelbox $70,000-$110,000. The price difference typically reflects (a) language_instruction review depth, (b) per-contributor consent harmonization, (c) RLDS schema validation, (d) SLA on delivery date adherence, and (e) indemnification rider terms.
Turnaround for VLA programs: pilot batch (200-500 episodes with full RLDS records and language_instruction labels) — Truelabel 7-14 days; Encord 14-21 days; Scale AI 30-60 days including onboarding. Full program (5,000-15,000 episodes): Truelabel 60-90 days; Encord 60-120 days; Scale AI 90-180 days. First-pass acceptance rate: Truelabel 92-97%; Encord 88-94%; Scale AI 90-96%; Appen 84-92%. The 4-7 percentage point spread on first-pass acceptance translates to $5,000-$25,000 in re-collection cost for a 10,000-episode program.
Sample QA gates for VLA training data
VLA training data has 8 acceptance gates beyond standard robotics-data QA: (1) RLDS schema compliance — every episode carries timestamp, robot_state, action, reward, language_instruction, is_terminal, plus optional fields (episode_id, observation_with_history) within the buyer's RLDS spec [1]; (2) language_instruction quality — instructions specific enough to disambiguate (object color, location, target receptacle, motion description) with reviewer agreement above 90%; (3) embodiment match — Franka Panda firmware, gripper SKU, kinematic calibration drift under 2 mm, joint-velocity logging at 30-50 Hz; (4) action-schema match — 6-DoF end-effector pose + gripper command, time-aligned within 5 ms; (5) sensor-fidelity gate — RGB at 1080p / 30 fps minimum, depth at 480p / 30 fps when applicable, audio at 44,100 Hz when relevant; (6) task-success gate — human-verified success on 100% of episodes with disagreement rate under 8% across 2 reviewers; (7) license + consent gate — single buyer-owned commercial-training license, 100% per-contributor consent artifacts; (8) coverage gate — at least 30 distinct objects, 5 lighting conditions, 3 background variations, 2 operator-skill levels.
Reject batches that miss gates (1), (3), (7); reject the program if gate (2) failure rate exceeds 12% or gate (5) exceeds 8%. A typical pilot of 200-500 episodes ships in 7-14 days at $750-$2,500; the full program of 5,000-15,000 episodes ships in 60-120 days at $25,000-$120,000. Skipping the language_instruction QA gate is the most common VLA-specific procurement mistake — programs that ship 5,000+ episodes with sub-12% language_instruction reviewer agreement routinely surface downstream model regression late, typically requiring re-labeling at 30-50% of original program cost.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- RLDS GitHub repository
RLDS defines a standardized record schema for robot learning datasets including timestamp, robot_state, action, reward, language_instruction, and is_terminal fields.
GitHub ↩ - OpenVLA: An Open-Source Vision-Language-Action Model
OpenVLA is a 7B-parameter vision-language-action model trained on 970,000+ episodes from Open X-Embodiment.
arXiv ↩ - Open X-Embodiment: Robotic Learning Datasets and RT-X Models
RT-2-X demonstrates positive cross-embodiment transfer when trained on Open X-Embodiment data.
arXiv ↩
FAQ
What's the best VLA training data provider for 2026?
It depends on the VLA family. For OpenVLA fine-tunes on Franka Panda, the canonical stack is Hugging Face cadene/droid + Open X-Embodiment Franka subset for pretraining + Truelabel for net-new commercial-license fine-tuning episodes. For WidowX 250, use BridgeData V2 + Truelabel. For humanoids, use AgiBot World + Scale AI or Truelabel custom programs.
How many demonstrations does OpenVLA need for fine-tuning?
Typical recipe: pretrain on 970,000+ OXE episodes + fine-tune on 5,000-15,000 net-new buyer-specific demonstrations at 30-50 Hz teleoperation cadence with RLDS-compliant delivery. Cost: $25,000-$80,000 from Truelabel-vetted partners.
What's the difference between OpenVLA, RT-2-X, π0, and GR00T data needs?
OpenVLA (7B params) was trained on 970K+ OXE episodes; RT-2-X (55B params) was trained on a 9-platform OXE subset; π0 was trained on a proprietary 10,000+ hour corpus; GR00T targets humanoid embodiments. Each requires different fine-tuning data: OpenVLA fine-tunes on 5K-15K episodes; RT-2-X needs 10K-30K; π0 typically requires partnership with Physical Intelligence; GR00T fine-tunes on humanoid-specific corpora like AgiBot World plus net-new capture.
Why does language_instruction quality matter so much?
Vague instructions ("pick the cup") generalize 25-45% worse on downstream tasks than specific instructions ("pick the red ceramic mug from the second shelf"). VLA programs that skip language_instruction review typically suffer 10-30% downstream model regression after deployment, which often forces re-labeling at 30-50% of original program cost.
What's the typical pilot turnaround for a VLA program?
Truelabel ships RLDS-compliant pilot batches of 200-500 episodes in 7-14 days at $750-$2,500. Encord ships in 14-21 days. Scale AI typically requires 30-60 days including onboarding. The pilot is the single best signal on full-program acceptance — skip it at 4-15x cost risk.
Can I mix open-license and commercial-license VLA data in one model?
Yes, with care. The standard 2026 hybrid recipe is: pretrain on Hugging Face open datasets (DROID, BridgeData V2) for the largest scale + fine-tune on Truelabel or Encord commercial-license episodes for the buyer's exact embodiment. The hybrid clears legal review when the final model weights are released only under the commercial-license terms covering the fine-tuning data.
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