I was going to argue the opposite that such high fidelity images are not really necessary to learn basic behaviour, in fact they look like overkill to me
Yeah image quality is irrelevant for training... but physics do. I was hoping they would have ultra-realistic physics engine.
Like have X amount of force applied to a ball in simulation and real-world and then watch as the simulation perfectly matches how the real world ball bounces around. That would be impressive. Instead it doesn't seem as they're adding that much more to a game engine other than a couple robot characters.
Synthetic training data. Sometimes it's cheaper / safer / easier to work with simulated data rather than real world sensor data. For some types of machine learning, a simulation is "real enough".
Someone asked me to build a startup doing the same. His most convincing use case was training camera systems to detect people falling overboard on cruise ships, etc. It's impractical / dangerous to use real world camera data for that.
There are other advantages, e.g., being able to generate simulations where everything is already labeled.
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[ 2.8 ms ] story [ 29.4 ms ] threadLike have X amount of force applied to a ball in simulation and real-world and then watch as the simulation perfectly matches how the real world ball bounces around. That would be impressive. Instead it doesn't seem as they're adding that much more to a game engine other than a couple robot characters.
Someone asked me to build a startup doing the same. His most convincing use case was training camera systems to detect people falling overboard on cruise ships, etc. It's impractical / dangerous to use real world camera data for that.
There are other advantages, e.g., being able to generate simulations where everything is already labeled.