
Developed a high-fidelity humanoid balance control simulation for the OpenHUTB/nn repository, focusing on reinforcement learning-based training and robust control systems. Over two months, migrated the simulation backend from PyBullet to MuJoCo, enabling more realistic physics and improved stability for dynamic balance tasks. Refactored the humanoid model and control logic in Python, enhancing gait stability and reducing falls through updated PID parameters and joint dynamics. Delivered an end-to-end experiment pipeline with expanded testing, regression checks, and comprehensive documentation, including multilingual support. The work established a reproducible platform for rapid prototyping in robotics, supporting future hardware integration and collaborative research efforts.
In May 2026, OpenHUTB/nn focused on delivering major improvements to humanoid stability and dynamic balance by refactoring the humanoid model, tuning the control system, and migrating the simulation backend from PyBullet to MuJoCo. The effort included extensive codebase reorganization, documentation updates, and stabilization fixes to support reliable RL-based balance control and more realistic physics.
In May 2026, OpenHUTB/nn focused on delivering major improvements to humanoid stability and dynamic balance by refactoring the humanoid model, tuning the control system, and migrating the simulation backend from PyBullet to MuJoCo. The effort included extensive codebase reorganization, documentation updates, and stabilization fixes to support reliable RL-based balance control and more realistic physics.
OpenHUTB/nn — April 2026 monthly summary: Consolidated humanoid balance control simulation in MuJoCo with RL-based training, including a backend migration from PyBullet, stability and gait improvements, and expanded tests and documentation. The deliverables establish a high-fidelity, reproducible RL experimentation platform that accelerates prototyping of balance control and informs future hardware integration.
OpenHUTB/nn — April 2026 monthly summary: Consolidated humanoid balance control simulation in MuJoCo with RL-based training, including a backend migration from PyBullet, stability and gait improvements, and expanded tests and documentation. The deliverables establish a high-fidelity, reproducible RL experimentation platform that accelerates prototyping of balance control and informs future hardware integration.

Overview of all repositories you've contributed to across your timeline