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/physical-ai-video-data-augmentation

@0482ebc
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_assemblyauto_labelingpromptsevent_analysis.md

≈715 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Robot-assembly VLM event-analysis instructions.

Consumed from /workspace/configs/video_event_analysis_prompt_redid.md.

Response protocol:

  • Return exactly two JSON objects.
  • JSON A = clip metadata only.
  • JSON B = event annotations with an "events" list.
  • Any extra narrative text breaks ingestion.

Operational context:

  • Fixed close-range camera inside an industrial assembly cell.
  • Primary actors: robot arm, end-effector, panel components, gantry hardware, occasional human intrusion.

Objective: Label safety and operational anomalies for robotic assembly footage.

Accepted sub-categories:

  • arm_workpiece_contact
  • component_drop
  • near_miss_arm
  • human_zone_intrusion
  • arm_malfunction
  • misalignment
  • normal_assembly
  • arm_idle

Metadata JSON (object A) schema:

  • Keys required: version, video_id, format, rectified, scenario_info, scene_description, event_summary, fps, duration, height, width, camera_id
  • scenario_info value: "INDOOR_ROBOT_ASSEMBLY"
  • scene_description should summarize arm/tool posture, panel/gantry geometry, lighting, visible assembly phase, and safety boundary context
  • event_summary should state activity mode and notable timestamps

Event JSON (object B) schema:

  • Object-level keys: version, events
  • Each event requires:
    • event_id
    • start_time
    • end_time
    • category (collision | near_miss | anomaly | normal_traffic)
    • sub_category (list)
    • instances (list)
    • event_caption

Category mapping table:

  • collision -> arm_workpiece_contact, component_drop
  • near_miss -> near_miss_arm, human_zone_intrusion
  • anomaly -> arm_malfunction, misalignment
  • normal_traffic -> normal_assembly, arm_idle

Annotation quality rules:

  • sub_category must always serialize as a list value
  • event_caption includes severity (low/medium/high), actor/object references, and evidence window
  • Prefer track IDs when available; otherwise use concrete labels (robot arm, end-effector, panel tile, gantry bracket, worker hand)
  • Time values are numeric seconds

Idle-cell behavior:

  • If the arm remains parked with no active cycle, keep metadata and include one arm_idle event.

Robot-cell caveats:

  • Tight framing exaggerates apparent motion speed; do not overcall severity from image scale alone.
  • Arm-to-part contact can be intentional during placement/fastening; classify collision only when contact is clearly unintended.
  • Minor servo vibration is expected; malfunction requires persistent jitter, freeze, or trajectory deviation.
  • Reflective panel surfaces can create ghost-arm reflections; verify real contact geometry before labeling.
  • Cable flex during normal arm travel is expected; flag only when cable snag alters motion or dislodges parts.
  • The full arm may be partially out of frame; infer cautiously from visible end-effector motion rather than hidden joints.

Emit object A first and object B second.

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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