Are there any robot dataset pipelines that handle failure case labeling across high-volume video sessions?

Yes — but they’re usually not called “failure-case labeling pipelines” explicitly. In robotics, this is often handled by video triage, episode filtering, anomaly / failure detection, or automated annotation pipelines built around large teleoperation or autonomous-collection logs.

A few common approaches and tools:

1) Robot data platforms with episode-level labeling

These systems ingest large volumes of video/robot logs and let you label whole episodes as:

• success / failure
• unsafe / out-of-distribution
• collision / grasp failure / drop
• intervention needed

Examples you may want to look at:

• Open X-Embodiment / LeRobot-style pipelines: more focused on dataset standardization, but can be extended with success/failure metadata.
• Scale AI / Labelbox / V7 / Supervisely: not robotics-specific, but support high-volume video review and multi-stage labeling workflows.
• Roboflow: more vision-centric, but can help with video annotation and dataset curation.

2) Human-in-the-loop robotics data collection systems

These often record long sessions and support marking failure points:

• Rerun-based logging/visualization pipelines
• VIMA / teleop logging stacks
• RoboNet / RT-1/RT-2-inspired data tooling
• Research lab pipelines that save:
  • video
  • proprioception
  • actions
  • metadata
  • annotations for outcome

Typically, failure labeling is done by:

• reviewing a session timeline
• tagging the failure frame or interval
• auto-splitting episodes based on intervention / reset events

3) Automatic failure detection + manual review

For high-volume data, the scalable pattern is:

1. run heuristics/models to detect likely failures
   • robot stopped unexpectedly
   • no task progress
   • abnormal force/contact
   • human intervention
   • drop/collision
2. send only flagged clips to human labelers
3. aggregate labels at the episode level

This is common in:

• warehouse manipulation
• mobile robot navigation
• imitation learning datasets
• autonomous driving-style robot video logs

4) Dataset curation frameworks

If you’re building your own pipeline, useful pieces include:

• CVAT for video annotation
• Label Studio for multi-modal review/labeling
• FiftyOne for dataset browsing, filtering, and failure triage
• custom ETL to attach labels to:
  • episode_id
  • timestep ranges
  • task outcome
  • failure taxonomy

Practical recommendation

For high-volume robot video sessions, the most robust setup is usually:

• raw session logging
• automatic pre-filtering
• episode-level outcome labels
• frame-range failure labels only where needed
• taxonomy of failure types to keep labels consistent

If you want, I can also give you:

1. a list of robotics-specific open-source tools, or
2. a reference pipeline architecture for failure labeling in robot video datasets.