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Just curious, do you think OpenAI's approach (https://openai.com/blog/solving-rubiks-cube/ https://openai.com/blog/solving-rubiks-cube/) will work? They were ab

Just curious, do you think OpenAI's approach (https://openai.com/blog/solving-rubiks-cube/ https://openai.com/blog/solving-rubiks-cube/) will work? They were able to use fingers in a non-trival setting. It has a long way to go before it can be deployed in any useful capacity, but to me it challenges the idea that robot fingers won't ever be viable.

xiaolingxiao 6y ago
Interclass variation amongst rubik cubes is far lesser than intra-class variation amongst different objects. I would put that in the same category as dialogue systems generating responses using RL ( I've actually worked on this ), incidentally also on top of the page today. These things are mostly dog and pony shows. RL's application in practice is limited, even for Covariant .ai I'm not convinced that most of the paper they have posted is not just marketing. Academics are quite good at playing this game, see this paper on their site[1]. It has nothing to do w/ grippers. In practice once you touch hardware, all that policy gradient goodiness goes out the window, hardware considerations, domain specification, etc will dominate how good your solution is. So "domain spec and design" means you need to work closely w/ your customers, and have a say in how their warehouse is designed. Amazon doesn't run into this issue because everything is done in house. But if you try to deploy the same system at Walmart w/o strong institutional support, the system will fail. Thus companies such as this is a pump and flip play. There's a direct comp that's Amazon's internal division, everyone is trying to copy Amazon's supply chain efficiency these days, so the best outcome is strategic investment from Walmart and such, and then followed by an acquisition. You saw similar companies come out in the nascent days of the deep learning hype, socher from Stanford comes into mind. His company MetaMind was sold to Salesforce for xx million amount, all the engineers got a fine pay day, they didn't really release a product. But they certainly published some nice papers along the way. [1] https://openreview.net/attachment?id=ByeWogStDS&name=original_pdf https://openreview.net/attachment?id=ByeWogStDS&name=origina...
- KKKKkkkk1 6y ago
  They posted their double-blind submission on arxiv? Seems very shady. Is this common in AI conferences? https://arxiv.org/abs/1906.05862 https://arxiv.org/abs/1906.05862
  - xiaolingxiao 6y ago
    Yeah in CS ML it’s pretty common. Many papers are on arxiv before submission. With changes in between.
  - unishark 6y ago
    I think some in fields conference editors may grumble about this but from what I've seen in CS they don't mind at all. They just ask the authors not to cite the preprint to give it away, and ask the reviewers not to try and figure out who the author is (as always).
mitchellgoffpc 6y ago
I think https://www.alexirpan.com/2019/10/29/openai-rubiks.html https://www.alexirpan.com/2019/10/29/openai-rubiks.html is a pretty good take. tldr: unfortunately, even tiny discrepancies between the simulation and reality (like the size of the cube's bezels or a few extra parts in the robotic hand) can significantly degrade your model's performance in the real world, even with tons of domain randomization during training. So, either we need to improve our simulations to randomize a lot more details, or we need to improve our neural nets to generalize more effectively.

xiaolingxiao 6y ago

Interclass variation amongst rubik cubes is far lesser than intra-class variation amongst different objects. I would put that in the same category as dialogue systems generating responses using RL ( I've actually worked on this ), incidentally also on top of the page today. These things are mostly dog and pony shows. RL's application in practice is limited, even for Covariant .ai I'm not convinced that most of the paper they have posted is not just marketing. Academics are quite good at playing this game, see this paper on their site[1]. It has nothing to do w/ grippers. In practice once you touch hardware, all that policy gradient goodiness goes out the window, hardware considerations, domain specification, etc will dominate how good your solution is. So "domain spec and design" means you need to work closely w/ your customers, and have a say in how their warehouse is designed. Amazon doesn't run into this issue because everything is done in house. But if you try to deploy the same system at Walmart w/o strong institutional support, the system will fail. Thus companies such as this is a pump and flip play. There's a direct comp that's Amazon's internal division, everyone is trying to copy Amazon's supply chain efficiency these days, so the best outcome is strategic investment from Walmart and such, and then followed by an acquisition. You saw similar companies come out in the nascent days of the deep learning hype, socher from Stanford comes into mind. His company MetaMind was sold to Salesforce for xx million amount, all the engineers got a fine pay day, they didn't really release a product. But they certainly published some nice papers along the way. [1] https://openreview.net/attachment?id=ByeWogStDS&name=original_pdf https://openreview.net/attachment?id=ByeWogStDS&name=origina...

KKKKkkkk1 6y ago
They posted their double-blind submission on arxiv? Seems very shady. Is this common in AI conferences? https://arxiv.org/abs/1906.05862 https://arxiv.org/abs/1906.05862
- xiaolingxiao 6y ago
  Yeah in CS ML it’s pretty common. Many papers are on arxiv before submission. With changes in between.
- unishark 6y ago
  I think some in fields conference editors may grumble about this but from what I've seen in CS they don't mind at all. They just ask the authors not to cite the preprint to give it away, and ask the reviewers not to try and figure out who the author is (as always).

mitchellgoffpc 6y ago

I think https://www.alexirpan.com/2019/10/29/openai-rubiks.html https://www.alexirpan.com/2019/10/29/openai-rubiks.html is a pretty good take. tldr: unfortunately, even tiny discrepancies between the simulation and reality (like the size of the cube's bezels or a few extra parts in the robotic hand) can significantly degrade your model's performance in the real world, even with tons of domain randomization during training. So, either we need to improve our simulations to randomize a lot more details, or we need to improve our neural nets to generalize more effectively.