Workcell black box
A replayed workcell incident should become evidence, not another raw log.
Metriplane turns replayed or calibrated workcell state into a physical event ledger, Cell Truth Report, portable evidence bundle, verification result, and generated regression check.
Built for teams that need incident context they can inspect, replay, verify, and keep as a generated regression check.
Archived author-run evidence Captured v0.2.0 values
Evidence workflow
The product surface is the chain of custody.
Within a configured domain pack, Metriplane packages bounded incident context so another reviewer can inspect, replay, verify, and preserve the event as a generated regression check.
- 01 replayed workcell state
- 02 physical event
- 03 incident
- 04 evidence bundle
- 05 verification
- 06 generated regression check
Generated artifacts
The output is designed for review, reproduction, and future checks.
Robotics teams often debug incidents using recorded sensor data, ROS bags, logs, traces, and simulation replays. Metriplane formalizes the next layer: incident context, evidence bundle, verification result, and generated regression check.
What it offers
A bounded evidence layer for robotics incident review.
Turn replayed state into an incident record.
Replay-derived physical events become a dated, inspectable timeline with process deviations and incident IDs.
Package the incident as portable evidence.
INC-0001 carries its manifest, checksums, incident JSON, event timeline, report, replay command, and limitations.
Keep the lesson as a generated regression check.
The missing-tool incident becomes missing_tool_caused_delay_INC-0001 and the generated regression check passes.
Public demos
Watch the evidence workflow, then inspect the lineage.
The current demo follows the SoftwareX manuscript artifact path: replayed assembly-cell state becomes an incident, evidence bundle, verification result, and generated regression check. The earlier demos stay visible as project lineage, not as unsupported current-release claims.
current release evidence loop Demo 08
Earlier public demos Lineage from camera tracking to evidence review.
Clean install, operator UI, live fusion, evidence export
The public release path shows setup, operator-facing UI, live fused state, and exported evidence artifacts.
Watch on YouTubeBackpressure and health degradation
The system demonstrates operational pressure, degradation signals, and health behavior under load.
Watch on YouTubeDeterministic replay and run provenance
Replayed runs preserve provenance so results can be compared, inspected, and repeated.
Watch on YouTubeEnd-to-end digital twin with multi-camera fusion
ROS 2 and Omniverse connect the physical scene, fused observations, and a live digital twin.
Watch on YouTubeMulti-camera world-coordinate sensor fusion
Multiple camera feeds are reconciled into shared world coordinates for a coherent cell view.
Watch on YouTubeFrom camera observations to ROS 2
The early pipeline turns observed marker state into ROS 2 messages and live system state.
Watch on YouTubeArUco-driven real-time digital twin in Omniverse
The first demo connects tagged objects to a live Omniverse digital twin.
Watch on YouTubeClaim to evidence
Public claims are mapped to concrete artifacts.
Archived author-run evidence is available for inspection. Fresh camera-free reproduction writes only to the documented temporary paths.
| Claim | Evidence | How to verify | Boundary |
|---|---|---|---|
| v0.2.0 is archived as a public release | GitHub release and Zenodo DOI 10.5281/zenodo.20736619 | Open the v0.2.0 release and Zenodo record. | SoftwareX acceptance or peer review is not claimed. |
| The archived author-run maintainer gate recorded 580 passed | Archived author-run evidence/paper_v2_0/test_output.txt reports 580 passed. | Inspect the archived file; do not use evidence/paper_v2_0/ as a fresh rerun destination. | The public independent macOS gate recorded 575 passed, 3 skipped, 2 failed; the public full suite is not a complete pass. |
| The camera-free replay is deterministic | deterministic_replay.txt reports pass=true, 0.0 cm position difference, and 0 event mismatches. | Run with RUNS=/tmp/metriplane-softwarex-runs and the checked-in demo session. | Checked-in demo session only; no live-camera claim. |
| The assembly-cell replay produces one incident | atlas_assembly_cell_run.txt reports 6 physical events, 1 process deviation, and 1 incident. | Run metriplane atlas run with the assembly_cell domain pack and --out /tmp/metriplane-softwarex-atlas. | One deterministic assembly-cell domain pack and replay. |
| INC-0001 is explainable as a Cell Truth Report | cell_truth_report.md records a 35.0 s wait for torque_driver_1. | Inspect archived evidence/paper_v2_0/atlas_run/cell_truth_report.md. | Derived from replayed planar state, not raw-video judgement. |
External review
Feedback is collected as technical review, not social proof.
External feedback is organized by theme and permission status. Named quotes require explicit permission; otherwise feedback is summarized anonymously.
Raw evidence layer
Robotics practitioners already use rosbags, logs, and traces as raw incident evidence.
Structured next layer
Metriplane is positioned as incident context -> bundle -> verification -> generated regression check.
Permission policy
Named quotes require permission. Otherwise feedback is summarized by theme.
Scope and non-claims
The project is intentionally bounded.
Metriplane is observe-only, local-first, camera-compatible and replay-oriented, planar/tagged-asset scoped, and research-software oriented.
Observe-only Planar/tagged-asset scoped Camera-free replay path No robot or machine controlNo safety certificationNo quality-release approvalNo people recognitionNo marker-free tracking claimNo full 3D reconstruction claimNo arbitrary anomaly-detection claimNo successful-remediation claimNo production-factory validationNo factory-wide deployment readinessNo complete external-validation claimNo SoftwareX acceptance or peer-reviewed publication claim
Next step
Inspect the artifact or send technical feedback.
Metriplane improves fastest when reviewers run the camera-free path, challenge the boundaries, and point out missing evidence.