KoalbyMQP/RaspberryPi-Code_24-25
Nov 2024 – Feb 2025
3 Months active
Languages Used
PythonURDF
Technical Skills
Control SystemsIMU IntegrationInverse KinematicsPID ControlPythonPython Scripting
Jai Jariwala
PROFILE
Jai Jariwala
Jay Jariwala developed advanced robotics control and simulation features in the KoalbyMQP/RaspberryPi-Code_24-25 repository, focusing on trajectory planning, balance automation, and reinforcement learning for humanoid robots. He enhanced walking and balance control by integrating IMU data, refining PID and MPC algorithms, and synchronizing real and simulated testing environments. Using Python, Casadi, and URDF, Jay implemented robust simulation pipelines, 3D visualization, and automated test suites to improve maintainability and deployment readiness. His work culminated in an end-to-end reinforcement learning solution for simulated robot balancing, reducing hardware testing cycles and establishing a scalable foundation for future robotics development and validation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
22Total
Bugs
0
Commits
22
Features
6
Lines of code
2,098
Activity Months3
Your Network
19 people
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered an end-to-end reinforcement learning (RL) solution to balance a humanoid robot in simulation within KoalbyMQP/RaspberryPi-Code_24-25. The work includes environment setup, RL agent definition, and a training script, with reset and restart capabilities to streamline iterative experiments. No major bugs fixed in this repo this month; the focus was on feature delivery and platform readiness. This work creates a scalable validation loop that reduces hardware testing cycles and accelerates future hardware integration, demonstrating strong RL, Python, and robotics-simulation skills.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered an end-to-end reinforcement learning (RL) solution to balance a humanoid robot in simulation within KoalbyMQP/RaspberryPi-Code_24-25. The work includes environment setup, RL agent definition, and a training script, with reset and restart capabilities to streamline iterative experiments. No major bugs fixed in this repo this month; the focus was on feature delivery and platform readiness. This work creates a scalable validation loop that reduces hardware testing cycles and accelerates future hardware integration, demonstrating strong RL, Python, and robotics-simulation skills.
February 2025
December 2024
16 Commits • 4 Features
Dec 1, 2024December 2024: Delivered robust MPC-based trajectory planning enhancements with extended horizon handling, state bounds, and time-varying parameters, plus RK4 integration and 3D visualization to improve planning clarity and execution accuracy. Refined walking/locomotion simulations and updated trajectories to enable safer, closer-to-real-robot testing. Implemented IMU-based balance automation with stand balance, squats, and assisted standing trajectories to boost stability and robustness. Completed test-suite cleanup and refactor to improve maintainability and demonstrate readiness. Overall, these efforts increased trajectory accuracy, robustness, and deployment readiness while expanding automation and testing capabilities.
December 2024
16 Commits • 4 Features
Dec 1, 2024December 2024: Delivered robust MPC-based trajectory planning enhancements with extended horizon handling, state bounds, and time-varying parameters, plus RK4 integration and 3D visualization to improve planning clarity and execution accuracy. Refined walking/locomotion simulations and updated trajectories to enable safer, closer-to-real-robot testing. Implemented IMU-based balance automation with stand balance, squats, and assisted standing trajectories to boost stability and robustness. Completed test-suite cleanup and refactor to improve maintainability and demonstrate readiness. Overall, these efforts increased trajectory accuracy, robustness, and deployment readiness while expanding automation and testing capabilities.
November 2024
5 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — KoalbyMQP/RaspberryPi-Code_24-25: Implemented enhancements to walking trajectory planning and IMU-based balance control. Key improvements include Y-axis IMU integration, PID tuning for balance, and synchronized testing across real hardware and simulation. Updated motor movement mapping to better reflect IMU axes and adjusted simulation parameters (P values and dt). These changes increase stability and repeatability of autonomous locomotion, reduce testing gaps, and establish a solid baseline for further motion and sensor fusion features.
5 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — KoalbyMQP/RaspberryPi-Code_24-25: Implemented enhancements to walking trajectory planning and IMU-based balance control. Key improvements include Y-axis IMU integration, PID tuning for balance, and synchronized testing across real hardware and simulation. Updated motor movement mapping to better reflect IMU axes and adjusted simulation parameters (P values and dt). These changes increase stability and repeatability of autonomous locomotion, reduce testing gaps, and establish a solid baseline for further motion and sensor fusion features.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture74.2%
Performance67.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonURDF
Technical Skills
CasadiCode CleanupControl SystemsData VisualizationFile ManagementIMUIMU IntegrationInverse KinematicsMPCMachine LearningMatplotlibMerge Conflict ResolutionModel Predictive ControlNumPyNumerical Simulation
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