
Learning a Probabilistic Latent Space of Object Shapes via 3D GAN - tacon
http://3dgan.csail.mit.edu/
======
visarga
So it can map between objects and latent representations both ways, and the
latent representations are disentangled (additive) - you can do vector math
with them to combine concepts.

I'm wondering how far we are from using such models to boost robotics. A robot
that understood the world around it would move much better and be able to
perform actions.

What is the bottleneck? Could it be that GANs are too slow for robotics, or
that we still can't control a humanoid to do basic tasks such as walking,
grasping, pushing and other manipulations?

From watching robot videos I get that we have dexterous robot arms and legs,
we just don't know how to use them to achieve useful things in unstructured
environments.

I'm sure there's a bottleneck somewhere or we'd have smart dexterous robots
today.

~~~
zitterbewegung
Being too slow for robotics is a extremely general statement. A robot that
needs to drive a car is very different than something that has to move things.

The bottleneck is the fact that this is a ongoing research topic and
integrating these things into a robotics system still requires effort.

~~~
visarga
> The bottleneck is the fact that this is a ongoing research topic

That's a general statement as well. It's like saying "the bottleneck is that
we don't know how to do it" which leaves the question unanswered: what is the
stumbling block, what is keeping us from being able to do it now? such as:
speed / sensors / not enough robots and funds for researchers / esoteric
machine learning considerations / something else.

I'm trying to understand the slow progress in robotics as contrasted to the
fast progress in deep learning.

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kozikow
It's exciting to see deep learning gaining ground in 3D. I'm wondering how
quickly until deep learning beats 3D reconstruction. State of the art
algorithm used in practical applications still does not use deep learning:
[http://www.gcc.tu-
darmstadt.de/media/gcc/papers/Waechter-201...](http://www.gcc.tu-
darmstadt.de/media/gcc/papers/Waechter-2014-LTB.pdf).

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aaronsnoswell
The exiting thing for me is at 1:30 in the video. Smooth changes in the latent
space lead to smooth changes in the shape (eg. the chair arms gradually recede
along their longitudinal axis). This means you have good generalisability (the
network is robust to input noise) and composablility (the network can produce
novel results through transformations in latent space).

