OpenHUTB/nn
Sep 2025 – Dec 2025
3 Months active
Languages Used
PythonXML
Technical Skills
Data VisualizationImitation LearningRobotics Simulation3D graphicsMuJoCoPython programming
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PROFILE
Blank885
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
4
Lines of code
1,714
Activity Months3
Your Network
159 people
Work History
December 2025
11 Commits • 2 Features
Dec 1, 2025December 2025 - OpenHUTB/nn monthly summary: - Key feature: PPO-based walking for Unitree H1 in MuJoCo with multi-phase trajectories and combined actions (walking, squatting, dancing, running, jumping), enabling richer locomotion data for training. - Expanded motion repertoire with double_action, triple.py iterations, and new actions (dance, back-and-forth sprint+jump, fall-and-rise). - Asset delivery: Unitree H1 model files uploaded to the repository to enable rapid experimentation. - Simulation enhancements: rolling_log interaction for improved visual/physical fidelity and updated configuration. - Testing and automation: perception-based stop testing (test.py) and ROS2 client support; stabilized code by resolving main.py merge conflicts and providing run-ready screenshots. Technologies/skills demonstrated: PPO reinforcement learning, MuJoCo physics, multi-action orchestration, Python tooling, ROS2, asset management, and modular action architecture.
11 Commits • 2 Features
Dec 1, 2025December 2025 - OpenHUTB/nn monthly summary: - Key feature: PPO-based walking for Unitree H1 in MuJoCo with multi-phase trajectories and combined actions (walking, squatting, dancing, running, jumping), enabling richer locomotion data for training. - Expanded motion repertoire with double_action, triple.py iterations, and new actions (dance, back-and-forth sprint+jump, fall-and-rise). - Asset delivery: Unitree H1 model files uploaded to the repository to enable rapid experimentation. - Simulation enhancements: rolling_log interaction for improved visual/physical fidelity and updated configuration. - Testing and automation: perception-based stop testing (test.py) and ROS2 client support; stabilized code by resolving main.py merge conflicts and providing run-ready screenshots. Technologies/skills demonstrated: PPO reinforcement learning, MuJoCo physics, multi-action orchestration, Python tooling, ROS2, asset management, and modular action architecture.
December 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025OpenHUTB/nn (2025-10) delivered a Dataset Visualization Script for LocoMuJoCo within the imitation-learning workflow. The script enables visualization of MuJoCo-based datasets directly in the environment, demonstrating dataset-driven experimentation with LAFAN1 and default squat motions. It validates data pipelines by instantiating the imitation-learning environment and rendering a short trajectory visualization, facilitating faster iteration and benchmarking.
October 2025
1 Commits • 1 Features
Oct 1, 2025OpenHUTB/nn (2025-10) delivered a Dataset Visualization Script for LocoMuJoCo within the imitation-learning workflow. The script enables visualization of MuJoCo-based datasets directly in the environment, demonstrating dataset-driven experimentation with LAFAN1 and default squat motions. It validates data pipelines by instantiating the imitation-learning environment and rendering a short trajectory visualization, facilitating faster iteration and benchmarking.
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 | OpenHUTB/nn | LocoMuJoCo Documentation scaffolding created to establish initial docs for imitation learning benchmarks using MuJoCo for complex motion tasks. No code changes; README placeholder added to LocoMuJoCo directory to define scope, usage, and next steps for benchmark development. Commit 8ca75d4a63b08ec705660c8050f2e3a6c54c9889 associated with the work (模仿学习基准专注于使用 MuJoCo执行复杂的运动任务 (#2564)).
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 | OpenHUTB/nn | LocoMuJoCo Documentation scaffolding created to establish initial docs for imitation learning benchmarks using MuJoCo for complex motion tasks. No code changes; README placeholder added to LocoMuJoCo directory to define scope, usage, and next steps for benchmark development. Commit 8ca75d4a63b08ec705660c8050f2e3a6c54c9889 associated with the work (模仿学习基准专注于使用 MuJoCo执行复杂的运动任务 (#2564)).
September 2025
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Quality Metrics
Correctness87.6%
Maintainability81.6%
Architecture81.6%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
PythonXML
Technical Skills
3D graphicsData VisualizationImitation LearningMuJoCoPython programmingROSRobotics Simulationalgorithm developmentdata processingdata visualizationgymnasiummachine learningphysics simulationreinforcement learningrobotics
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