
Google Brain releases two large datasets for robotics research - hurrycane
https://plus.google.com/+VincentVanhoucke/posts/8T7DSJhGY3u
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AndrewKemendo
I'm actually really excited about this because this is the kind of data that a
tiny startup couldn't just get and helps people compete. I am still worried
that nobody will be able to compete with the big guys long term in RL, simply
because they have the money and access to build these huge training sets, but
at least this gives someone somewhat of a chance.

Kudos to Vincent, Sergey, Chelsea and Laura for promoting openness in ML
research!!

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ericjang
The resources one has at his or her disposal dictate their solution space, and
not necessarily the quality of the solution. Google is uniquely good at doing
stuff at scale. Some day a grad student who doesn't have the money to buy a
GPU might instead invent a form of sample-efficient RL that the big guys never
even thought of :)

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misiti3780
Link to datasets:
[https://sites.google.com/site/brainrobotdata/home](https://sites.google.com/site/brainrobotdata/home)

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Apofis
Damn, 800,000 grasp attempts? Pretty nuts that they need this many... but I
guess without tactile feedback, only relying on visual feedback you need this
many attempts to get it right.

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samscully
Babies take a while to learn how to grasp things, it's believable that the
number of attempts they make is in the 10,000s. 1 grasp attempt a minute, 8
hours a day for 3 months is 43,000 attempts.

Of course they are learning a lot of other stuff at the same time so it's not
really comparable.

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taneq
This is something that I think people often miss when they compare machine
learning to human performance. Humans spend a LOT of time in their early
learning and calibrating phase. Like, it's our full time job, 365 days a year,
for several years. One interaction every ~5sec, for 12 hours a day, seems a
modest estimate. That's over 3 million training examples per year.

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epberry
Awesome - I think this could be really useful for action recognition.
Collecting large video datasets is really challenging and google's robot array
is a great way to repeatedly create these kinds of custom datasets.

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IshKebab
Very cool, but isn't this a reinforcement learning task? Don't you need access
to the machines to get them to learn?

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gwern
Only for some algorithms. Many RL algorithms can learn off-policy. Or you can
treat it as a supervised problem: "given this image, predict the taken
action". (Think of AlphaGo initially being trained to predict human players'
next moves based on a large KGS corpus of games; no interaction or access to
Go games required.)

Admittedly, I'm not entirely sure what you would _do_ with any of that after
all the learning is done, if you don't eventually have one of those very
expensive robot arms to use.

