Eval data for robotics

Quick facts

Request type

EVAL

Scope

Small fixed bundle for review

Data

Egocentric, teleop, manipulation, or custom modality

Turnaround

Short pilot before larger capture

Acceptance

Buyer reviews sample against checklist

Comparison

FAILURE & RECOVERY DATA

Failure and recovery demonstrations: a sourced atlas

Failure and recovery data is demonstrations of a robot getting a task wrong and correcting it. This atlas maps that emerging data class: every row ties a cited dataset or paper to failure type, task, embodiment, onset, root cause, recovery outcome, real or sim, modalities, and rights, with license and consent kept separate.

The eight-record sample is directional, not a complete or representative census. It covers the robot failure dataset, robot failure detection dataset, robot recovery data, failure-correction trajectories, and recovery demonstration dataset intents while keeping every unverified field explicit.

Exports: JSON · CSV. For comparison prose rather than atlas records, see common failure modes in ego/exocentric data.

Next steps: compare public corpora, check dataset fit, or request held-out failure and recovery data. Sample packets come before scale, with rights, consent, and per-trajectory provenance attached.

When to start with eval data

Start with eval data when the buyer needs evidence quickly: a sample of supplier quality, a held-out benchmark, or a narrow slice of a larger environment before committing to a full capture program. Real-world robot datasets such as DROID show how much scene and task diversity can matter before scale ^[1], while Open X-Embodiment shows why cross-robot format coverage should be checked early ^[2]. CALVIN-style zero-shot evaluation also makes held-out language, environment, and object conditions explicit before a buyer treats a sample as production-ready ^[3].

"Binary success is too coarse for long-horizon control, we therefore use a stage-wise scoring scheme."

That is the signal an eval request should create: not just pass/fail, but enough scoring detail to decide whether to refine the spec, change supplier, or scale capture.

What makes eval data useful

Useful eval sets are small but specific. They should preserve the metadata, rights, consent, and QA standards expected from a larger dataset so the buyer can trust the signal before scaling. Stress cases matter: THE COLOSSEUM shows that environmental perturbations can cut manipulation success rates sharply ^[5], and ManipArena argues real-world execution exposes perception noise, contact dynamics, hardware constraints, and latency that simulator-only eval misses ^[6]. Delivery should preserve synchronized logs and schemas, with formats like expert-led model evaluation workflows or MCAP-style timestamped multimodal data keeping review artifacts attached to the eval result ^[7].

Eval data vs training data

Training data teaches a model; eval data measures whether it generalizes. Eval sets should be held out, deduplicated from training, and designed around task success, failure modes, environment splits, and scoring rules rather than simply being more examples from the same distribution. Teams buying VLA training data, teleoperation traces, or robot demonstrations should define the eval split before suppliers scale collection.

Robotics eval dataset taxonomy

Useful eval bundles include supplier QA samples, held-out deployment scenes, regression suites, perturbation/OOD sets, safety or edge-case reviews, format-validation sets, VLA instruction-following evals, and navigation route evals. Each needs required fields, labels, metrics, and rejection reasons. A format-validation eval should load the sample in the LeRobot format or target schema before any broader buy is approved.

Leakage-resistant eval design

Hold out environments, objects or SKUs, contributors, time periods, lighting conditions, route IDs, instruction paraphrases, and failure cases. Ask suppliers to attest that eval examples were excluded from training and provide enough metadata to audit overlap. For marketplace requests, include the leakage audit in the robot training data marketplace acceptance rubric and keep rights review linked through egocentric data licensing when people or workplaces appear.

Eval-set acceptance checklist

A quotable robotics eval bundle should include held-out split definition, task instructions, embodiment, environment/object split keys, metric or rubric, failure taxonomy, leakage attestation, timestamps, action/state schema when applicable, provenance files, and a small load test. Reject eval samples that duplicate training scenes, omit failure cases, hide labels, or cannot be scored by an independent reviewer.

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

   DROID reports 350 hours of robot manipulation data across 86 tasks.

   arXiv ↩
2. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

   Open X-Embodiment provides standardized robotic learning data across many robots, skills, and tasks.

   arXiv ↩
3. CALVIN paper

   CALVIN evaluates agents zero-shot on novel instructions, environments, and objects.

   arXiv ↩
4. LongBench: Evaluating Robotic Manipulation Policies on Real-World Long-Horizon Tasks

   LongBench uses stage-wise scoring because binary success is too coarse for long-horizon control.

   arXiv ↩
5. THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation

   THE COLOSSEUM evaluates manipulation models across 14 environmental perturbation axes.

   arXiv ↩
6. ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation

   ManipArena targets real-world evaluation gaps caused by perception noise, contact dynamics, hardware, and latency.

   arXiv ↩
7. MCAP file format

   MCAP stores multiple channels of timestamped multimodal log data for robotics applications.

   mcap.dev ↩
8. truelabel egocentric data glossary

   Internal contextual link to the egocentric data definition.

   truelabel.ai
9. truelabel sourcing brief intake

   Internal contextual link to Truelabel's sourcing intake workflow.

   truelabel.ai
10. truelabel egocentric warehouse video sourcing spec

    Internal contextual link to warehouse egocentric video sourcing.

    truelabel.ai
11. truelabel egocentric kitchen video sourcing spec

    Internal contextual link to kitchen egocentric video sourcing.

    truelabel.ai
12. truelabel industrial egocentric video sourcing spec

    Internal contextual link to industrial egocentric video sourcing.

    truelabel.ai
13. truelabel warehouse robotics data sourcing

    Internal contextual link to warehouse robotics data sourcing.

    truelabel.ai
14. truelabel kitchen manipulation data sourcing

    Internal contextual link to kitchen manipulation data sourcing.

    truelabel.ai
15. truelabel LeRobot dataset alternative comparison

    Internal contextual link to the LeRobot dataset alternative comparison.

    truelabel.ai
16. truelabel eval data for robotics hub

    Internal contextual link to robotics eval data sourcing.

    truelabel.ai
17. truelabel hand-object interaction data page

    Internal contextual link to hand-object interaction training data requirements.

    truelabel.ai
18. truelabel egocentric video datasets hub

    Internal contextual link to the egocentric video datasets hub.

    truelabel.ai

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