Just some observations from an ex autonomous robotics researcher here. One of the most important differences at least in those days (80's and 90's) was time. W

Just some observations from an ex autonomous robotics researcher here. One of the most important differences at least in those days (80's and 90's) was time. While the digital can be sped up just constrained by the speed of your compute, the 'real world' is very constrained by real time physics. You can't speed up a robot 10x in a 10.000 grabbing and stacking learning run without completely changing the dynamics. Also, parallellizing the work requires more expensive full robots rather than more compute cores. Maybe these days the different ai gym like virtual physics environments offer a (partial) solution to that problem, but I have not used them (yet) so I can't tell. Furthermore, large scale physical robots are far more fragile due to wear and tear than the incredible resilience of modern compute hardware. Getting a perfect copy of a physical robot and environment is a very hard, near impossible, task. Observability and replay, while trivial in the digital world, is very limited in the physical environment making analysis much more difficult. I was both excited and frustrated at the time by making ai do more than rearanging pixels on a 2D surface. Good times were had.