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This is so cool! Although, I'm sure it will take quite a bit of time to actually use these robots in everyday settings. I was wondering if they could create mu

by an4rchy 11y ago

This is so cool! Although, I'm sure it will take quite a bit of time to actually use these robots in everyday settings. I was wondering if they could create multiple copies of robots and give them all different tasks and then combine the knowledge, to speed up the process. However, due to randomness of learning I would assume each task has to be taught to one single robot or it might not be as efficient? Also, I'm curious to know, would a robot need to relearn a skill if a part like the arm changes shape because the basic building block on which it relies on has changed?

PeterisP 11y ago
For your question, "would a robot need to relearn a skill if a part like the arm changes shape", for the currently commonly used style of machine learning which is also implied by the article - deep convoluted neural networks - the situation is that it would need some relearning/retraining but it would be much smaller than starting from scratch. When you need to train a system for problem X that limited data available, a possible approach is to train a system on some related problem Y where the data is plentiful, and then use that network (with e.g. the final layer removed) as input for a smallish network that solves the problem you need, but piggybacks on all the patterns discovered by the original network. In a similar manner, if you had a system that works on one style of arms, then it would be usable training a (much smaller) neural network that "converts" from that system to outputs of another style of arms.
- bargl 11y ago
  This may be a simple question. Can you take "snapshots" of AI and then load multiple robots with the same robot model learned in a classroom like this? I.E. Do the training with a prototype robot, then send the learned behavior to a factory where they can send out pre-taught robots with what the prototype learned? I can't see a reason this wouldn't work, but maybe I'm missing something.
  - robotresearcher 11y ago
    Yes, absolutely. With almost all implementations, any learned/trained parameters are stored in a file or database and can be backed-up, restored, or transferred to a duplicate. Some systems are sensitive to precise calibrations of sensors and actuators, which must be done per-device. But usually this stuff is low-level detail that is decoupled from the AI stuff.

by an4rchy 11y ago

PeterisP 11y ago

For your question, "would a robot need to relearn a skill if a part like the arm changes shape", for the currently commonly used style of machine learning which is also implied by the article - deep convoluted neural networks - the situation is that it would need some relearning/retraining but it would be much smaller than starting from scratch. When you need to train a system for problem X that limited data available, a possible approach is to train a system on some related problem Y where the data is plentiful, and then use that network (with e.g. the final layer removed) as input for a smallish network that solves the problem you need, but piggybacks on all the patterns discovered by the original network. In a similar manner, if you had a system that works on one style of arms, then it would be usable training a (much smaller) neural network that "converts" from that system to outputs of another style of arms.

bargl 11y ago
This may be a simple question. Can you take "snapshots" of AI and then load multiple robots with the same robot model learned in a classroom like this? I.E. Do the training with a prototype robot, then send the learned behavior to a factory where they can send out pre-taught robots with what the prototype learned? I can't see a reason this wouldn't work, but maybe I'm missing something.
- robotresearcher 11y ago
  Yes, absolutely. With almost all implementations, any learned/trained parameters are stored in a file or database and can be backed-up, restored, or transferred to a duplicate. Some systems are sensitive to precise calibrations of sensors and actuators, which must be done per-device. But usually this stuff is low-level detail that is decoupled from the AI stuff.