robot-recovery-bench 0.1.2

Validate robot recovery segment JSONL and summarize intervention outcomes and timing.

robot-recovery-bench

robot-recovery-bench validates JSONL records for human intervention and robot recovery segments, then summarizes outcome and timing metrics without loading robot media.

At a Glance

Job	Make recorded intervention and recovery segments structurally reviewable and comparable.
Built for	Robotics data curators, HIL researchers, teleoperation teams, and training-data reviewers.
Differentiator	Dependency-free schema checks and aggregate metrics over metadata-only segment records.
Produces	Validation errors or Markdown/JSON recovery metrics and failure-reason clusters.

Install

python -m pip install "robot-recovery-bench==0.1.2"

Verified Quickstart

Run from a source checkout:

robot-recovery-bench validate examples/mock_recovery_segments.jsonl

robot-recovery-bench report examples/mock_recovery_segments.jsonl \
  --format json \
  --out /tmp/robot-recovery-report.json

The bundled fixture validates three segments and reports a recovery success rate of 0.6667 and a training-ready rate of 0.6667.

Record and Metric Contract

Each segment records an episode ID, task, failure reason, intervention type, start/intervention/end timestamps, and recovery result. Optional fields include operator action and whether the segment is marked training-ready.

Reports include:

• segment count;
• recovery success and training-ready rates;
• average time to intervention and recovery duration;
• failure-reason cluster counts.

The JSON metric intervention_rate is 1.0 whenever segments are present because every input row is already an intervention segment. It is not an episode-level, dataset-level, or fleet-level intervention prevalence estimate.

Python adapters can normalize LeRobot intervention lists and RLDS steps into the segment shape; the CLI itself accepts JSONL.

Runtime, Data, and Network Boundary

• Validation and reporting read local JSONL and write a local aggregate report.
• The package does not load video, connect to a robot, execute a policy, or make network requests.
• No redaction is applied. Aggregate reports can preserve failure-reason labels, and validation output identifies line numbers, so review labels before sharing results.
• Metrics describe only the supplied segments and do not infer unrecorded failures, operator quality, causal safety, or real-world recovery capability.

Limitations

• The CLI accepts normalized JSONL segment records only. Upstream conversion from RLDS, LeRobot, or in-house formats remains the caller's job.
• Aggregate rates are segment-level summaries, not episode-level or fleet-level reliability claims.

Compatibility

robostudio-engine exposes a direct robostudio recovery integration that imports this package when installed or available in the AuraOne monorepo.

Publication Status

Verified on 2026-07-13:

• PyPI: robot-recovery-bench==0.1.2
• GitHub release: v0.1.2
• Bundled segment records are synthetic tutorial data, not benchmark evidence.

Next Action

Normalize one reviewed batch of intervention segments, validate it, and inspect the failure-reason clusters before deciding whether any records are training-ready.