Whilst this is interesting, this isn't where the field is going. This is useful if, say, you want to make a CNC robot with extreme rigidity. Replacing forward

Whilst this is interesting, this isn't where the field is going. This is useful if, say, you want to make a CNC robot with extreme rigidity. Replacing forward and inverse kinematics with machine learning is extremely simple, and avoids most of the problems these formulas have (for instance, they don't consider that a robot might attempt to move through itself). And while grasping and manipulation isn't simple even with machine learning, I think you'll agree that the theory in these books also very, very much isn't simple. And the problem is that the current capabilities of robots using these formulas from books like these ... are insufficient. They need to become WAY more complex to be useful, but that's impossible: they're already damned complicated. The outlook very much is that the only way we'll ever arrive at the required complexity for these machines is through machine learning, and if not that, then through some form of automated program writing. A human just cannot write safe and working code that deals with robots in open settings and/or where interactions might occur. You should probably still learn the very basic parts. Chapter 9 and 11. But after that, you will likely get further with machine learning.