OpenHUTB/nn
Dec 2025 – Dec 2025
1 Month active
Languages Used
MarkdownPythonXML
Technical Skills
MuJoCoPythonPython programmingalgorithm developmentalgorithm optimizationautomation
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PROFILE
Ywwbzd
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
3
Lines of code
5,721
Activity Months1
Your Network
159 people
Work History
December 2025
16 Commits • 3 Features
Dec 1, 2025December 2025 — OpenHUTB/nn delivered a robust robot arm grasping simulation platform from project setup to advanced grasping workflows. Major deliverables included: 1) Robot Arm Grasping Simulation — project initialization, MuJoCo integration, and robust file handling for reliable runs; 2) Control Tuning and Compatibility — PID parameter tuning and error-threshold adjustments that significantly improved stability and responsiveness; 3) Advanced Grasping System — collision detection, multi-angle visualization, multi-mode control, gradual force application, automated tasks, and resilient error handling; 4) Stability and Usability improvements — removal of GLFW dependency, simplified input, and documentation enhancements. Impact: enables rapid experimentation, reduces setup friction, and provides a production-ready simulation foundation for automated grasping tasks, delivering tangible business value through improved realism, reliability, and automation capabilities. Technologies demonstrated: MuJoCo, PID control, collision detection, multi-view visualization, multi-mode control, automation, and lean dependency management.
16 Commits • 3 Features
Dec 1, 2025December 2025 — OpenHUTB/nn delivered a robust robot arm grasping simulation platform from project setup to advanced grasping workflows. Major deliverables included: 1) Robot Arm Grasping Simulation — project initialization, MuJoCo integration, and robust file handling for reliable runs; 2) Control Tuning and Compatibility — PID parameter tuning and error-threshold adjustments that significantly improved stability and responsiveness; 3) Advanced Grasping System — collision detection, multi-angle visualization, multi-mode control, gradual force application, automated tasks, and resilient error handling; 4) Stability and Usability improvements — removal of GLFW dependency, simplified input, and documentation enhancements. Impact: enables rapid experimentation, reduces setup friction, and provides a production-ready simulation foundation for automated grasping tasks, delivering tangible business value through improved realism, reliability, and automation capabilities. Technologies demonstrated: MuJoCo, PID control, collision detection, multi-view visualization, multi-mode control, automation, and lean dependency management.
December 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture82.4%
Performance81.2%
AI Usage36.2%
Skills & Technologies
Programming Languages
MarkdownPythonXML
Technical Skills
MuJoCoPythonPython programmingalgorithm developmentalgorithm optimizationautomationcontrol systemsdata visualizationdocumentationerror handlingfile managementroboticssimulationvisualization
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