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by NVIDIA Corporationnvidia/skills3.5k stars
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Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.

Use this Skill: https://skilld.dev/gh/nvidia/skills/physical-ai-video-data-augmentation

This session only. Nothing lands on disk.

assetscookbooksrobot_assemblyREADME.md

≈1.3k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Robot Assembly Dataset

Scene description

A fixed close-up camera monitors an industrial robotic assembly cell. The frame is dominated by a robot arm with an end-effector approaching a blue tiled panel array (solar panels or similar modular components) mounted on a metal gantry/support structure. The gantry features grey steel vertical posts and horizontal crossbeams with mounting brackets, bolts, and cable runs. The camera captures the robot arm's workspace at close range, showing the arm joints, end-effector, panel tile surfaces, and structural hardware in detail. Industrial overhead lighting illuminates the cell.

Augmentation variables

Variable Options Default weights Rationale
lighting bright, moderate, dim 0.35 / 0.35 / 0.30 The only meaningful appearance axis for a controlled indoor assembly cell. Cosmos can vary the overall illumination intensity — bright (full overhead fixtures), moderate (balanced ambient), or dim (reduced lighting with task-light emphasis). No weather or time-of-day variables apply indoors with no exterior windows.

Tuning guide

See the shared parameter reference in ../TUNING_GUIDE.md.

Scene-specific notes:

  • lighting is the only augmentation variable; scale n_augmentations up to recover diversity instead of adding unsupported scene variables.
  • Keep detection_and_tracking.classes intentionally narrow (person) because robot components are not COCO-80 classes.
  • Use higher vlm_json.frame_fps when validating fast arm motion anomalies.

Key decisions & warnings

Decision Choice Rationale Risk if wrong
Augmentation variables lighting (1 variable only) Fully indoor controlled environment with no exterior windows. Lighting intensity is the only visual dimension Cosmos can meaningfully vary. Weather and time_of_day do not apply. Single variable limits augmentation diversity. If more variety is needed, consider adding a second pass with different sigma values rather than a second variable.
Variable options & weights lighting: bright 0.35 / moderate 0.35 / dim 0.30 3 distinct lighting levels. Source footage appears moderately lit, so even distribution. Too many fine-grained options → model can't reliably distinguish them
Detection classes [person] Only COCO-80 class relevant for safety. The robot arm, solar panels, and gantry have no matching COCO-80 classes. Human zone intrusion is the critical safety event that detection can catch. No tracking for the robot arm itself — all arm-related events rely entirely on VLM analysis. If no humans ever appear, the detector produces zero detections, which is expected.
max_age 30 Static close-up scene with no exit/re-entry pattern. If a person enters, they either stay visible or leave. Too high would create ghost tracks from brief partial detections of the robot arm or its shadow
frame_fps / sampling_fps 6 Robot arms move quickly — an arm collision or malfunction can occur in under a second. 6 fps captures sufficient temporal detail. Too low → a quick collision or jerk is missed between frames; too high → unnecessary token cost for idle periods
Event types collision: arm_workpiece_contact, component_drop; near_miss: near_miss_arm, human_zone_intrusion; anomaly: arm_malfunction, misalignment; normal_traffic: normal_assembly, arm_idle 8 sub-categories covering robotic assembly safety and operational events. Missing event type → safety incidents go unlabeled; wrong category → MCQ and event JSON disagree

Scene-specific warnings:

  • COCO-80 has no robot arm class: The robot arm, end-effector, panels, and gantry are all invisible to the object detector. ALL assembly-related events (collisions, malfunctions, misalignments) depend entirely on VLM event analysis. Only human intrusion benefits from bounding-box detection.
  • Close-up framing: The camera is very close to the workspace. This is unusual compared to typical surveillance setups. The VLM may describe the scene differently from wide-angle footage, and captions may focus heavily on mechanical detail rather than spatial layout.
  • Intended contact is the norm: During normal assembly, the robot arm touches the panels and brackets intentionally. The VLM must distinguish intended assembly contact from unintended collisions — this is a subtle judgment that may produce false positives for arm_workpiece_contact.
  • Reflective panel surfaces: The blue tiled panels are reflective and may create confusing mirror images of the robot arm. The VLM should not interpret reflections as additional objects or misalignment.
  • Single variable limits augmentation diversity: With only lighting as a variable, the augmented dataset has lower visual diversity than outdoor scenes with 2 variables. Consider running more augmentations per video (n_augmentations: 3–5) to compensate.

File inventory

Standard cookbook layout: see ../FILE_INVENTORY.md.

Robot-assembly specifics: single augmentation variable lighting; event_analysis.md defines 8 event types across 4 categories; question_bank.json holds 10 questions covering safety, lighting, and assembly status.

Source: SKILL.md on GitHub

2 warnings3mo3 checks · Risk SAFE
  • Gen Agent Trust Hub3mo

    The skill is a workflow orchestrator for video data augmentation and auto-labeling on the NVIDIA OSMO platform. It manages the end-to-end pipeline, including credential verification, configuration generation, worker execution, and result retrieval. No security issues or malicious patterns were detected; all external dependencies and network operations are associated with trusted vendors and the skill's primary functionality.

  • Socket3mo

    3 alerts: gptAnomaly

  • Snyk3mo

    Risk: MEDIUM · 1 issue

Signed by skilld at 0482ebc. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 4 months ago
Other metadata
metadata
{
  "owner": "NVIDIA",
  "service": "data",
  "version": "1.0.0",
  "reviewed": "2026-05-26",
  "author": "NVIDIA",
  "tags": [
    "physical-ai",
    "video-data-augmentation",
    "auto-labeling",
    "cosmos"
  ]
}

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