Ask HN: If deep learning is so effective why dont Boston Dynamics robots use it? - osipov
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1arity
I think the biggest thing Boston Dynamics are dealing with ( aside from power
density ) are improving balance and running algorithms, under the stresses of
heavy loads, non-homogenous terrain, and loss of friction.

It's possible to use deep learning for controlling the servo motor positions
and torques, the hierarchy of features is then varying levels of abstraction
over the set of positions. The most granular layer may be features related to
all the torques and positions of the servo, a higher layer may be some
combinations of subsets of these, and the highest level may be features akin
to the general representation of the spatial position and tension of the limb.

It's quite naturally similar to how human kinetic intelligence and perception
works, say you are learning how to dance, you can be aware of the position of
all of your limbs simultaneously ( the general pose you are adopting ), or you
can be aware of the angle and position of your ankle or your right hip
independently ( what your foot is doing ).

I think there could be things to learn from the networks in octopus limbs --
because of their extensive innervation.

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skythomas
I don't know the real answer in Boston Dynamics' case. However, in general,
the answer is that Deep Learning is just beginning to be integrated into the
pipelines of major robotics/reinforcement learning pipelines. The same thing
took time in complicated speech recognition pipelines.

Deep learning is great for recognition/classification but robotics is much
more.. Deep learning has to coexist with a number of other critical
components.

However, there is major and extremely exciting work being demonstrated in the
literature art recent conferences on this front. Expect major developments
this year

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dennybritz
I don't know anything about Boston Dynamics Robots, but "Deep Learning" is not
a full solution to building robots. Deep Learning architectures may be used
for sub-problems that robots need to solve, e.g. image classification, object
recognition, or other classifications tasks where hand-designing features is
hard and enough data is available.

Robots are a complex system with lots of moving pieces. Often they use some
form of reinforcement learning (which in turn may use Deep Learning for
estimating rewards) to decide which actions to take.

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Yadi
It can be computationally expensive for a robot to use any sorts of deep
learning on board for improvement during work.

It takes hours if not days sometimes to train a good scaled model. Then
retraining the model could take a quarter of that time, which is too much
delay for a robot. However, with linear models things can be easier.

Also, there are very few people who know how to manage DNN or creating neural
nets from scratch in a proper required way. Otherwise Boston Dynamics have to
stick to one of the existing libraries which wouldn't be ideal.

DNN is amazing for some work that am sure they must be using them already, for
example models that can help them with the feature engineering and such.

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vonnik
OP: Just curious, how do you know they don't use deep learning?

@opless: Deep learning may be a fad in the press, but there's substance
underneath. It's been breaking one accuracy record after another in raw,
unstructured media year after year. ImageNet is just one deep convolutional
net competing against another. A lot of other ML algorithms have hit a
ceiling, and the ceiling is tied to not having enough humans to engineer
enough features. DL doesn't have that chokepoint, and it is able to process
vast seas of data that other algos have trouble with. Therefore, for many
problems, it will continue to win.

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opless
Deep learning is just a fad.

Throw enough data at your favourite ML algorithm and you'll get better
results.

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skythomas
Uhm.. no. What is special today about deep learning is the hierarchy of
features. Yes, we are talking primarily about CNN's and RNN's trained with SGD
TODAY. Yes,the entire catalog of ML techniques are still very interesting.
Yes, in the future many of these techniques will be viable in a deep setting.
But, no, you don't just get state of the art results by using linear
regression or HOG and SIFT SVM's on a big data set.

Hierarchy of features is important.

