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if you're wondering why lots of these differentiable pipelines are tasked with learning physics (and what that has to do with google) the answer is that this is
by anon_tor_12345 5y ago
if you're wondering why lots of these differentiable pipelines are tasked with learning physics (and what that has to do with google) the answer is that this is "compute oriented development". by "compute oriented development" i mean that since google has access to unlimited compute they can use this compute to run physics kinematics solvers (ie pde solvers) that are then used to generate training data for RL models. what's the point of the RL model if the physics model already exists and gives you high fidelity simulations? well it's clearly an easy paper to write... but other than that, some people claim the RL models are faster than the physics solver. i guess that's true if you don't take into account the millions of hours of compute spent on the solvers themselves.
- nmca 5y ago(disclaimer: work on RL, have trained models for simulated tasks) I'm fairly sure that people work on control because general algorithms for control would be very useful (e.g., robot that can skin a cat and drive a car by holding the steering wheel). Such a robot would exist in our 3d physical world, so simulations of of our 3d world are used for training. If this could be done with radically less compute, it would be.
- anon_tor_12345 5y agosure but it doesn't hurt that you have infinite data too (i.e. the thing most other ML research is bound by). like you can't argue that it's not a very comfortable corner to be in wrt being able to publish.
- kadoban 5y agoSounds quite a bit like you're complaining that they chose/engineered a fruitful field of study. I think I'm missing what the problem with that is.
- anon_tor_12345 5y ago>I think I'm missing what the problem with that is. I'm complaining that publishing endless papers on your methods that are trained on endless amounts of synthetic data is more about paper churn than contributing something novel. like the person below says: no real control system uses an RL controller (e.g. boston dynamics uses only classical controls).
- kadoban 5y agoOh I see. I would have guessed that that was because this way relatively new. If it really doesn't translate to anything real then I definitely get your point.
- velcroscientist 5y agoGood point. This is why robotics researchers do not take deep RL papers seriously unless they have some real world robotics results. I'm looking at you, people who only show mujoco results and claim their algorithm is useful for robotics. Simulators are useful though for real world robotics. You can prototype your environment and algorithm, and also attempt sim2real transfer. For example, use the simulator to generate a lot of image data, and train image based controllers. Add enough domain randomization and maybe your controller trained on the simulator can transfer to real images.