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>We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation Simulat
by daenz 7y ago
>We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation
Simulated training is so cool. Related, is anyone interested in a plugin for Blender that allows you to easily build physically-accurate simulation environments for robots and then apply reinforcement learning to the virtual robots? I have a hodge-podge amount of code for doing exactly this, and I'm curious if anyone else would be interested in it?
- askytb 7y agoYes, absolutely
- artemish 7y agoYes please!
- deleted 7y ago[deleted]
- sgillen 7y agoPersonally I think it would be useful to focus on the "easily build physically accurate simulation environments for robots" using blender part. IMO it makes the most sense to try and make this created environment into an OpenAI gym environment so that way most of the existing RL algorithms can be applied to the robots. If you do want to spin your own RL this approach does not stop you from doing so. AFAIK there are a lot of publicly available RL algorithms out there, but not many (any) blender like interfaces to make physically accurate simulations.
- carapace 7y agoAlso yes, please!
- RugnirViking 7y agoYes, I'd be interested.
- ofou 7y agoAlso Yes, please!
- orasis 7y agoThe trick here isn’t “accurate” simulation, it’s that they used a bunch of different simulations with randomly perturbed physics and the RL learned policies that worked across these wide range of “realities”.