19,723 research outputs found
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
The ability to interact and understand the environment is a fundamental
prerequisite for a wide range of applications from robotics to augmented
reality. In particular, predicting how deformable objects will react to applied
forces in real time is a significant challenge. This is further confounded by
the fact that shape information about encountered objects in the real world is
often impaired by occlusions, noise and missing regions e.g. a robot
manipulating an object will only be able to observe a partial view of the
entire solid. In this work we present a framework, 3D-PhysNet, which is able to
predict how a three-dimensional solid will deform under an applied force using
intuitive physics modelling. In particular, we propose a new method to encode
the physical properties of the material and the applied force, enabling
generalisation over materials. The key is to combine deep variational
autoencoders with adversarial training, conditioned on the applied force and
the material properties. We further propose a cascaded architecture that takes
a single 2.5D depth view of the object and predicts its deformation. Training
data is provided by a physics simulator. The network is fast enough to be used
in real-time applications from partial views. Experimental results show the
viability and the generalisation properties of the proposed architecture.Comment: in IJCAI 201
Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map
Whileas the Kohonen Self Organizing Map shows an asymptotic level density
following a power law with a magnification exponent 2/3, it would be desired to
have an exponent 1 in order to provide optimal mapping in the sense of
information theory. In this paper, we study analytically and numerically the
magnification behaviour of the Elastic Net algorithm as a model for
self-organizing feature maps. In contrast to the Kohonen map the Elastic Net
shows no power law, but for onedimensional maps nevertheless the density
follows an universal magnification law, i.e. depends on the local stimulus
density only and is independent on position and decouples from the stimulus
density at other positions.Comment: 8 pages, 10 figures. Link to publisher under
http://link.springer.de/link/service/series/0558/bibs/2415/24150939.ht
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