Dashboard / Repos / Mylerobot

Mylerobot

C+ 70 completed
Ai Ml
containerized / python · small
198
Files
33,467
LOC
2
Frameworks
6
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#369992
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
58.47
Framework unique
—
Isolation
—
Last stage change
2026-05-10 03:35:31
Deduplication group #49932
Member of a group with 4 similar repo(s) — canonical #84285 view group →
Top concepts (2)
Project DescriptionWeb Backend
Open data scored by Repobility · https://repobility.com

AI Prompt

Create a project for real-world robotics using LeRobot. I need a structure that incorporates state-of-the-art machine learning techniques. The project should be containerized, and I'd like to include examples for building an SO-101 robot and potentially extending it to a mobile platform like LeKiwi. Please ensure the setup supports testing using pytest and provides clear documentation using markdown and YAML files.
python robotics machine-learning flask pytest docker ai containerization
Generated by gemma4:latest

Catalog Information

LeRobot is a machine learning project for real-world robotics using state-of-the-art techniques.

Description

LeRobot is an open-source project that leverages the power of machine learning to enable real-world robotics applications. Built on top of PyTorch, it provides a robust framework for developing and deploying AI-powered robots. With its focus on practicality and ease of use, LeRobot aims to bridge the gap between theoretical research and actual implementation.

الوصف

هو مشروع مفتوح المصدر يستخدم قوة التعلم الآلي لتمكين تطبيقات الروبوتات في العالم الحقيقي. بني على أساس باي تورش، يوفّر إطارًا متينًا للتنمية وتوزيع الروبوتات المزودة بالذكاء الاصطناعي. مع تركيزه على الواقعية والسهولة الاستخدام، يسعى لربط الفجوة بين البحث النظري والإجراءات الحقيقية.

Novelty

7/10

Tags

machine-learning real-world-robotics pytorch artificial-intelligence deep-learning

Technologies

flask huggingface pandas pytorch

Claude Models

claude-opus-4.6

Quality Score

C+
70.3/100
Structure
81
Code Quality
64
Documentation
83
Testing
75
Practices
54
Security
62
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (35% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • 2376 duplicate lines detected \u2014 consider DRY refactoring
  • 15 'god files' with >500 LOC need decomposition

Recommendations

  • Address 118 TODO/FIXME items \u2014 consider tracking them as issues

Security & Health

36.1h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (3)
Source: Repobility analyzer · https://repobility.com
MIT
License
16.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
85.4%
markdown
10.0%
yaml
1.7%
html
1.7%
json
0.8%
toml
0.4%

Frameworks

Flask pytest

Concepts (2)

Repobility · code-quality intelligence · https://repobility.com
CategoryNameDescriptionConfidence
Want fix-PRs on findings? Install Repobility's GitHub App · github.com/apps/repobility-bot
auto_descriptionProject Description![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml?query=branch%3Amain) ![Coverage](https://codecov.io/gh/huggingface/lerobot) ![Python versions](https://www.python.org/downloads/)80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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