Alternative robot arm ML approaches

The realm of robot arm control via machine learning is really just getting started. There are a couple of research avenues I wanted to bring to your attention as you look for further study. One way to approach our understanding of robot movement is to consider the balance between exploitation and exploration. Exploitation is getting the robot to its goal as quickly as possible. Exploration is using the space around the robot to try new things. The path-planning program may have been stuck on a local minimum (think of this as a blind alley), and there could be better, more optimal solutions available that had not been considered.

There is also more than one way to teach a robot. We have been using a form of self-exploration in our training. What if we could show the robot what to do and have it learn by example? We could let the robot observe a human doing the same task, and have it try to emulate the results. Let’s discuss some alternative...