This is the perfect opportunity to pitch my own version of this robot! Implemented in a ROS framework, with a working walking gait, and some decent documentation: https://github.com/mike4192/spotMicro
I stumbled on nicrusso7's github page as I was doing my own initial research. I initially, naively, thought I could easily expand on his work and reinforcement learn my way to a working walking gait pretty quickly. However once I got a feel for the hardware and it's performance limits and the play in the system, I realized differences between the simulated model and real life would be significant, and would make debugging the translation from simulation to real life difficult. Also I'm teaching myself software development and reinforcement learning was another whole thing to learn.
So instead I took a different approach and implemented a more "conventional" gait, with inspiration and examples from other similar projects.
I've abandoned reinforcement learning for now, as I'm more interested in implementing mapping and motion planning going forward. But one day I'd like to revisit it and see how the pipeline for ML to real implementation works for something like this low level control.
I stumbled on nicrusso7's github page as I was doing my own initial research. I initially, naively, thought I could easily expand on his work and reinforcement learn my way to a working walking gait pretty quickly. However once I got a feel for the hardware and it's performance limits and the play in the system, I realized differences between the simulated model and real life would be significant, and would make debugging the translation from simulation to real life difficult. Also I'm teaching myself software development and reinforcement learning was another whole thing to learn.
So instead I took a different approach and implemented a more "conventional" gait, with inspiration and examples from other similar projects.
I've abandoned reinforcement learning for now, as I'm more interested in implementing mapping and motion planning going forward. But one day I'd like to revisit it and see how the pipeline for ML to real implementation works for something like this low level control.