[mike4921]

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.

[nicrusso7]

Well done! Btw I do agree with you regarding the knowledge transfer on the real robot platform. The open source design is built to work with very cheap servos and the alinement is not quite easy too.

As you, I was totally new to RL when I started (almost 1 year ago) - I’m a DevOps engineer during the day :) The goal of this project for me is learning ML more than build a product - that’s why the limited focus on hw implementation. Anyway, there are examples on the web on how to run the policies on the real world robot (even using ROS) - maybe in the future I’ll digging in this topic as well!