Enhance Humanoid Robot Gait

I already have a 19-DOF humanoid running on a custom Python HAL that talks seamlessly to both MuJoCo and our Unitree H1. A deterministic 200 Hz loop handles PD control, torque limits, e-stop logic, and a multi-layer safety stack, yet the walking pattern still feels rough. The immediate goal is to redesign or retune the gait-generation module so the robot moves with greater stability and energy efficiency without losing real-time performance.

All of the middleware—Python, NumPy, FastAPI, WebSockets—along with MuJoCo’s API is in place, so you will be dropping your solution straight into an existing repository. You should be comfortable profiling and optimising code that must never miss its 5 ms deadlines, and you’ll need to validate results first in MuJoCo, then on the physical H1.

Deliverables
• New or modified gait-generation algorithm integrated into my Python control framework
• Demonstration video and log files from both simulation and hardware runs, showing stable, repeatable walks at various speeds
• Brief write-up of approach, key parameters, and how to tweak them in future

Acceptance criteria
1. Loop frequency remains ≥ 200 Hz measured over 60 s continuous operation
2. No foot slippage or falls in a 3 min MuJoCo walk and a 1 min H1 walk on flat ground
3. Energy consumption (integrated torque) improves on current baseline by at least 10 % in simulation

When you reply, please include links or short clips of past work on biped or quadruped gait planning, real-time control, or related projects—seeing previous successes will speed up selection.