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zqc3117/Spotter

Hardware, IoT & roboticsPython
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Official codebase for Spotter: Let the Embodied Model Lead, and the VLM Reflect for It

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About the project

Spotter is a robotics research framework that adds a lightweight intervention layer between a frozen embodied model (System 1) and a VLM (System 2), letting the policy lead execution while the VLM steps in only to confirm and repair errors.

Useful for
  • Run π0.5 or Cosmos Policy evaluation on RoboCasa with parallel VLM supervision
  • Plug a custom embodied model into Spotter's intervention layer for error repair
  • Reproduce the paper's 1,200-episode RoboCasa experiments with a local Qwen

README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.

3.7 Early signal Magnitude out of 10 — how fast interest is growing, not a quality score.

Star growth looks organic. Data as of October 5, 2026, 12:02 UTC.

The star-growth assessment does not verify whether the project is safe to run.

Stars per day

04080September 27, 2026October 5, 2026

Bars show daily stars; the line is a moving average of the available history. Short histories do not yet establish a reliable usual pace. Red marks spike days.

Why it’s trending

  • Star-counter measurements on 2026-10-05 (UTC), 10:59–11:48: 97 → 99 stars (+2). This is the change over that interval.
  • Estimated end-of-day forecast: about +20 stars, using observed gains and the previous day.
  • The repository is 6 days old and already has 99 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet.
  • Top new repositories this week: #128.

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Three GitHub discoveries in each edition

What they do, why they are gaining interest, and what to check before using them.

Editions at 10:00, 16:00, 22:00 MSK.

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Numbers

Total stars
99
Today
+2 stars measured 2026-10-05, 10:59–11:48 UTC, ≈ 20 by evening
Forks
2
Issues and pull requests
4
Watchers
2
Language
Python
License
MIT
Latest release
v1.0 · September 29, 2026
Created
September 29, 2026
Last push
October 5, 2026

Star trust

Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.

These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.

Spotted in
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