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MetriPlane v0.2.0 external technical feedback
I am collecting external technical feedback for a SoftwareX research-software paper and MSc thesis.
MetriPlane v0.2.0 is an open-source, observe-only physical-observability tool for bounded workcells. Feedback can be a short technical reaction, not necessarily a full reproduction.
3-minute demo:
https://www.youtube.com/watch?v=7U5nbBbGGbw
Repository:
https://github.com/Miko997/metriplane
Reproduction issue:
https://github.com/Miko997/metriplane/issues/6
Zenodo DOI:
https://doi.org/10.5281/zenodo.20736619
I am not asking for a recommendation. I am collecting reproducibility, relevance, limitation, and research-software feedback.
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* Indicates required question
Name or anonymous label
*
Use your real name only if you are comfortable. Anonymous is also fine.
Your answer
Role / title
For example. Robotics engineer, PhD student, professor, simulation engineer, perception engineer, software engineer
Your answer
Organization
Your answer
Public profile or website
LinkedIn, GitHub, personal website, lab page, or company profile. Optional.
Your answer
What did you review or run?
*
Watched the 3-minute demo
Inspected the GitHub repository
Inspected the Zenodo release
Ran python -m metriplane.cli doctor
Ran deterministic replay
Ran Atlas assembly-cell workflow
Ran evidence bundle verification
Ran generated regression test
Only giving a technical opinion
Other:
Required
If you ran any commands, what was the result?
Pass
Fail
Not run
Not sure
doctor
deterministic replay
Atlas assembly-cell run
bundle verify
generated regression test
Pass
Fail
Not run
Not sure
doctor
deterministic replay
Atlas assembly-cell run
bundle verify
generated regression test
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Operating system
Your answer
Python version
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In 2–5 sentences, does this artifact seem technically relevant?
*
For example: relevance to robotics, simulation, manufacturing, digital twins, physical AI, perception, workcell review, or research-software reproducibility.
Your answer
What is the main limitation or next validation step?
*
Critical feedback is preferred. Please mention what should be improved, bounded, measured, or validated next.
Your answer
Is the framing “physical observability for replayable workcell evidence” understandable?
*
Yes, clear
Mostly clear
Somewhat unclear
Unclear
Not sure
Is the incident → evidence bundle → regression test loop useful?
*
Loop: replayed workcell state → physical event → evidence bundle → verification → generated regression test
Yes, clearly useful
Potentially useful
Interesting but needs more validation
Unclear
Not useful in its current form
What was confusing, missing, or poorly explained?
Your answer
How may I use your feedback?
You may quote or summarize my feedback with my name
You may quote or summarize my feedback anonymously
Please ask me before quoting or summarizing
Private feedback only
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May I acknowledge your feedback in the repository, paper notes, or thesis notes?
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Yes, with my name
Yes, anonymously
Please ask first
No
Contact email for follow-up
Optional. Leave blank if you do not want follow-up.
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