I'm building a quadrupedal robot that will learn how to walk. From the responses I got from asking if its possible to run a NN on a micro controller I realised I needed to think of a clever system that wouldn't take 1000 years to be effective and would still be able to demonstrate onboard learning. I've designed a system but I'm not sure how effective it will be.
Firstly I hardcode 5-20 positions for the legs.
I set up a (simple) neural network where each node is a different set of positions for the legs, which I will write.
The robot moves from one node to another and the weight of the joint is determined by how far forward the robot moves.
Eventually there will be strong connections between the best nodes/positions and the robot will have found a pattern of moves that are most successful in walking.
How effective would this be in learning to walk?
Note: instead of positions I could write short gaits and the process would work out which sets work best when combined.