PUBLIC DATASET — NOT a TRADES DATA capture. PUBLIC DATASET processed by the TRADES DATA pipeline to demonstrate the pipeline — this is NOT TRADES DATA captured footage. RGB + our overlays (hand landmarks + object masks) only; no MANO/SMPL mesh.
Attribution (CC BY 4.0): HO-Cap (CC BY 4.0) — Wang et al., HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction. irvlutd.github.io/HOCap · source
overlays: hand landmarks + object masks (no MANO/SMPL mesh) · PII review: PASSED_FACE_BLUR · LICENSE: docs/legal/external/HoCap-license.md

Buyer dataset viewer

hocap-demo-01

Raw clip, action evidence, WHY chain, worker review, closure audit, and standards status.

not ready0/0 standards passing; 0 blocking pending
actionTimelinerequires worker review and downstream verificationwhyClaimsblockedagenticWhyblockedreviewQuestionsnot computedworkerReviewnot computedclosurenot computedrawIntegritynot computedsemanticIndexnot computedstandardsnot computedbundlenot computedlegalnot computeddatasetCardnot computed
truth states0
dataset cardpending

Action timeline

Truth-state spans

0:00.0

Sensor evidence overlay

Audio and IMU evidence tied to player time

alignment not computed
Audio waveform
no published markers
Audio extract not computed; PCM materialized: no; 0 public alignment anchors; per-stream impulse count not published.
Helmet IMU jerk
no published markers
Cross-stream alignment not computed; 0 public alignment anchors; per-stream impulse count not published.
Wrist IMU jerk
no published markers
Sensor manifest not computed; 0 public alignment anchors; per-stream impulse count not published.
Phone IMU + events
no published markers
Sensor manifest not computed; 0 public alignment anchors; per-stream impulse count not published.

Signal-quality preview — what a buyer evaluates before signing

Clip stats, action-span profile, and cross-stream agreement (anti-fabrication evidence)

duration
fps
resolution
action spans11
labeled time0.4 min
alignment

Action-span length distribution

  • <1s0
  • 1-3s11
  • 3-10s0
  • 10-30s0
  • >30s0

Cross-stream timing agreement

Cross-stream alignment artifact is not published for this clip.

Selected span WHY detail

wait

0:00.0-0:02.0
description
worker sits at table with right hand resting on surface; no object interaction occurs during this interval
truth state
inferred candidate
confidence
0.80
granularity
5.00s

Evidence chain

No evidence chain rows published for this span.

WHY claims and citations

No promoted WHY claims overlap this span.

Worker review

No worker-review question overlaps this span.

Closure audit

Publish readiness

not computed
Closure audit artifact is pending at /lab-clips/closure-audit/hocap-demo-01.json.

Audit depth — the verifiability moat

Verify it yourself: provenance, measured accuracy, signing

show, don't tell

Every shipped decision carries a re-derivable provenance record, a worker-confirmed accuracy measurement, and an honest signing status — rendered exactly as produced. Missing surfaces render as missing, never mocked.

Per-decision provenance — the verifiability moat

Verify it yourself

Per-decision provenance surface is not published for this clip yet. Expected at /lab-clips/provenance/hocap-demo-01.json. It renders as missing, never mocked.

Audited linkage — the per-decision spine

One spine, two products

Audited-linkage surface is not published for this clip yet. Expected at /lab-clips/audited-linkage/hocap-demo-01.json. Renders missing, never mocked.

Measured accuracy (worker-confirmed)

/lab-clips/measured-accuracy/hocap-demo-01.json

computed: present
Buyer-ready?buyer_acceptance: presentgated from buyer delivery
measured_accuracy
{
  "accuracy": null,
  "artifact_truth_state_counts": {
    "action-timeline": {
      "_empty": 1
    },
    "why-claims": {
      "_empty": 1
    }
  },
  "caveats": [
    "n_worker_answers=0: no calibration data yet; scaffold only."
  ],
  "clip_id": "hocap-demo-01",
  "delta_truth_state_counts": {},
  "n_distinct_workers": 0,
  "n_worker_answer_deltas": 0,
  "schema_version": "measured-accuracy-v1",
  "status": "SCAFFOLD_NO_CALIBRATION_DATA",
  "wilson_bounds": {
    "estimate": null,
    "lower": 0,
    "upper": 1
  }
}
rung_coverage
{
  "action-timeline": {
    "field_count_by_truth_state": {
      "_empty": 1
    },
    "total_fields": 1
  },
  "why-claims": {
    "field_count_by_truth_state": {
      "_empty": 1
    },
    "total_fields": 1
  }
}
methodology_notes
[
  "Worker-confirmed label accuracy = fraction of worker_answer deltas where the auto-label was CONFIRMED UNCHANGED (WORKER_CONFIRMED). Corrections (WORKER_REJECTED) count against.",
  "Conformal calibration (MAPIE/crepes) requires a real calibration set; current bounds are Wilson interval prior — not calibrated.",
  "PROV blocks are W3C-PROV terms as dict keys (no rdflib). Render as RDF-PROV if a full graph is required."
]

C2PA / provenance signing status

Missing artifact

missing
C2PA provenance surface is not published for this clip yet. Expected at /lab-clips/c2pa-provenance/hocap-demo-01.json.

Raw integrity

Missing artifact

missing
Raw integrity artifact is missing. Expected at /lab-clips/raw-integrity/hocap-demo-01.json.

Semantic index

Missing artifact

missing
Semantic index artifact is missing. Expected at /lab-clips/semantic-index/hocap-demo-01.json.

Bundle status

Missing artifact

missing
Buyer bundle manifest is missing. Expected at /buyer-bundles/hocap-demo-01/tier1-raw/manifest.json.
Worker review · how we interrogate ambiguity
Sample processing — the worker-review session for this clip is generated once the clip is processed. We render nothing until real claims exist: no fabricated questions, no placeholder answers.

Modality matrix

Present, missing, or degraded streams

Helmet RGBmissing sensor

not published

confidence ceiling not published
Helmet audiomissing sensor

not published

confidence ceiling not published
Head IMUmissing sensor

not published

confidence ceiling not published
Wrist IMUmissing sensor

not published

confidence ceiling not published
Phone IMUmissing sensor

not published

confidence ceiling not published
Smart toolmissing sensor

not published

confidence ceiling not published

Standards scorecard

All dataset standards

0 rows
IDStandardObservedTargetVerdict

Why this dataset — verifiability beats claims

Differentiators, honest limitations, and how to buy more

modalities present0
distinct action classes3diversity weighted ≥ volume
labeled spans11variable-granularity, evidence-spanned
cross-stream alignmentper-stream measured
truth-stated fields0

The differentiators (lead with how quality is enforced)

Dexterous, full-per-finger representation + wrist 6-DoFRobotics

We ship rich human (21-keypoint + per-joint angles + wrist 6-DoF), you retarget to your hand — the field-standard interface (HumanEgo/EgoVLA/EgoScale), not pre-retargeted to one robot.

Cross-modal-VERIFIED contactRobotics

Contact is keypoint-in-named-mask cross-checked by IMU/audio — 2D overlap alone is wrong on near-misses; ours is sensor-corroborated, with the per-moment agreement shown in the signal-quality panel.

Per-decision audit depth (verify it yourself)Both

Every claim drills to its receipts and re-derives; the Merkle root recomputes in your own browser. Fine-grained queryable provenance is a documented unmet bar — it is the premium tier here.

Worker-conversation loop — expert-confirmed WHYBoth

The reasoner interrogates its own uncertain claims, asks the highest value-of-information question, and a worker's gated answer flips the field to WORKER_CONFIRMED (or holds it DEGRADED if unearned) — the elicited intent/rationale footage structurally cannot contain. Wired on the live path; the promotion gate refuses any synthetic/unbound identity, so a confirmation requires a real worker bound by the authenticated web layer — never faked.

Honesty / truth-states (missing → missing)Both

Every field carries MEASURED/INFERRED/WORKER_CONFIRMED/DEGRADED/UNKNOWN/REJECTED + confidence + provenance. A model-card-grade limitations section is buyer risk-reduction, not weakness.

Consent / PII compliance gateBoth

Fail-closed audio consent, PII-blur every frame, lawyer-reviewed contracts — your training-data documentation de-risking (EU AI Act), made a selling feature.

Two altitudes from one captureRobotics

The manipulation tier (humanoid-core) and the project-execution tier (heterogeneous-team orchestration) — the second shipped INFERRED / north-star, honestly.

Honest limitations — stated, then refuted

  • Object 6-DoF + near-field depth are DEGRADED (monocular, non-metric).The wrist IMU anchors global wrist pose independent of vision (survives occlusion), and every pose ships with per-moment cross-modal confidence — the differentiator monocular-only cohorts lack. We never label a monocular guess MEASURED.
  • Corpus scale is early — few fully-populated clips today.Raw-data volume is not the moat; generalization follows a power law in environment/object DIVERSITY (Lin et al., ICLR 2025), and the scarce, un-replicable real-world distribution + expert-confirmed WHY is the defensible asset. We sell the data + audited annotations, never the model.
  • Dense reward-progress is not in the export yet.It is EVAL-GATED: the TopReward layer ships on the LeRobot/RLDS export ONLY after it clears our eval (construction-egocentric transfer is unproven). Until then the channel is an honest placeholder — never a fabricated dense signal.

How we argue value

We report in the vocabulary you trust — real-world-success correlation + MMRV (SIMPLER / WorldEval lineage): “policies trained on our data rank / perform correctly,” not a bespoke internal score. Model-lift is reported per bundle where available — never asserted without the comparison run behind it.

Buy more — the round-trip

  • Request a slice / label / trade — tracked requested → in-production → delivered.
  • Subscription, not a file — changelog, version tags, refresh cadence; newer data is one click.
  • Quality is contractual + visible — per-field truth-state + confidence + dated quality history; a failed gate shows REJECTED, never silently shipped.
  • The corpus is flywheel fuel — each worker correction compounds into reusable, citable rules; honest scarcity is the moat.

Datasheet (Gebru et al.) + Croissant 1.1 + load-in-3-lines

Dataset card — credibility + machine-discoverable metadata

1 · Motivation

purpose
Robotics human-demonstration dataset + an insurance safety-observation feed — one scene understanding, two scope-tagged products.
product surface
not published
intended use
buyer evaluation, model fine-tuning, design-partner pilot, insurer loss-control review

2 · Composition

modalities present
see modality presence matrix
feature columns
not published
proprio statistics
not published
truth-state distribution
not published

3 · Collection process

worker consent
not published
audio consent
not published
camera calibration
not published
reproducibility
not published

4 · Preprocessing / cleaning / labeling

PII redaction
not published
C2PA / provenance
not published
model versions
not published
git sha
not published

5 · Uses

format export ceiling
not published
not-intended use
re-identification, worker discipline, redistribution beyond license, treating DEGRADED/UNKNOWN as ground truth
cancelled / excluded surfaces
world-model, dense mesh, digital twin, robot policy (per cancellation boundary)

6 · Distribution

license
not published — not published
license posture
not published
serializations
LeRobot v3 (canonical), RLDS / Open-X, sim-ready kinematic export
license chain
not published

7 · Maintenance

codebase version
not published
generated at
not published
decision-precedent coverage
not published
closure audit
not published

Two altitudes from one capture

Manipulation tier · PRIMARY

Humanoid-core: per-finger articulation + wrist 6-DoF + cross-modal-verified contact + the action/observation representation a robotics buyer trains on. MEASURED/INFERRED per field.

Project-execution tier · INFERRED / north-star

The team-orchestration graph for heterogeneous robot teams (who does what, in what order, with what dependencies). Shipped INFERRED / north-star and labeled as such — never asserted MEASURED.

Load in 3 lines

LeRobot v3 (canonical)

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("trades-data/hocap-demo-01")           # LeRobot v3 layout (in the buyer bundle)
print(ds[0]["observation.truth_state"], ds[0]["action.label"])

RLDS / Open-X

import tensorflow_datasets as tfds
ds = tfds.load("trades_data_hocap_demo_01")    # RLDS / Open-X episode-step
for step in next(iter(ds["train"])).numpy(): ...      # same columns + truth_state passthrough

Croissant 1.1 metadata header

License + modalities + size + task categories + consent flags as machine-actionable usage policies. This is the metadata header; the full RecordSet ships inside the LeRobot/RLDS export.

{
  "@context": {
    "@vocab": "https://schema.org/",
    "cr": "http://mlcommons.org/croissant/",
    "conformsTo": "dct:conformsTo",
    "dct": "http://purl.org/dc/terms/"
  },
  "@type": "Dataset",
  "conformsTo": "http://mlcommons.org/croissant/1.1",
  "name": "trades-data-hocap-demo-01",
  "description": "TRADES_DATA Tier-1 human-demonstration dataset (robotics) + safety-observation feed (insurance) from a single audited scene understanding. Every field carries a per-field truth-state + confidence + provenance.",
  "license": "see dataset card",
  "version": "unversioned",
  "keywords": [
    "robotics",
    "human-demonstration",
    "egocentric",
    "construction-trades",
    "manipulation"
  ],
  "creator": {
    "@type": "Organization",
    "name": "TRADES_DATA"
  },
  "cr_size": {
    "feature_columns": 0,
    "proprio_statistic_columns": 0
  },
  "cr:usageInfo": {
    "two_party_consent_audio": "see dataset card",
    "pii_redaction": "see dataset card",
    "worker_consent": "see dataset card",
    "holdout": "clips flagged holdout in the DB never enter any buyer bundle (write-path guard)",
    "not_for": [
      "re_identification",
      "worker_discipline",
      "redistribution_beyond_license"
    ]
  }
}