A couple of advanced data science algorithms. Implemented both for the walking table. ARS is great. We hear a lot about deep learning. This one is shallow learning, and does very well on simpler tasks. Just inputs and outputs, no hidden layers.
It’s similar to the Evolution Strategies algorithm. Generally trying some random stuff out, and slowly changing the model based on what gets you closer to the goal.
After much confusion, I made a table with legs that can move.
So I raised the table to z=1 plane and put the origin of the joints in the right place. THEN you set the legs/links with origin at -0.5 because the joint is at 1, and the leg is 1 long, and presumably it needs 0.5 because that’s the centre of the box.
I did visualisation by changing the filename in this file in the bullet3 folders, and running in python: