Hercules
Machine learning software to help AI control bipedal robots and hands
A sophisticated software suite designed to bridge the gap between machine learning models and physical robotic hardware, specifically optimized for bipedal systems and robotic hands. This assists AI in controlling robotics.
The primary difficulty lay in translating complex simulated training data into stable, real-world movements for bipedal structures, which are inherently unstable. I had to manage high-frequency data streams while ensuring low-latency communication between the AI controller and the hardware actuators.
I developed a robust framework using Python and PyTorch that utilizes reinforcement learning to stabilize gait and manipulation. By implementing a modular systems architecture, I created a pipeline that allows for rapid iteration between simulation environments and physical deployment.
The system successfully demonstrated improved stability in bipedal locomotion and more fluid, natural movements in robotic hand coordination. The repository serves as a scalable foundation for future research into autonomous humanoid movement and tactile sensing.
