@phdthesis {pub2795,
	title = {An Object Representation and Methods for Uncertainty Aware Shape Estimation and Grasping},
	author = {Stanimir Dragiev},
	year = {2014},
	month = {October},
	abstract = {One of the keys to understanding intelligence is the experience of reproducing it, building it
into systems we create. Robotics is the natural ground to implement, test, evaluate and realise
concepts. It has already taught us that intelligence is not solely a matter of high cognition, but
implies understanding of seemingly trivial everyday skills like walking, sentiment detection and
interaction with the physical world.
This thesis introduces an internal object representation for the purpose of robotic manipulation.
It abstracts the physical appearance of objects and rather considers a function which describes
the surface implicitly. The developed methods for building such models employ Bayesian
statistical approaches to fuse the information sources {\textendash} different sensors and a priori knowledge
{\textendash} and estimate the form of an object being aware of the uncertainties.
The functions have such a shape that can be interpreted as potential field generated by the
object. A controller uses this to navigate a robot arm for grasping and manipulation. Since
the representation translates the uncertainty of the sensors into confidence of the model, an
improved controller is able to employ this in order to achieve more robust grasping or more
efficient estimation of an object. This links to exploration-exploitation notions related to decision
theory.
Finally, the grasp and estimation methods are integrated to systems used to demonstrate or
quantify in simulated and real environments the benefits and limitations of the representation.
The key beliefs and insights the thesis builds on and attempts to convey are that sensing
and control must benefit from each other; embodiment {\textendash} own and environmental limitations {\textendash}
are important for learning; object models need to be aware of uncertainty and expose it; uncertainty
is motion feature {\textendash} must be used to improve control; the real world eventually requires
uncertainty aware hardware.},
	publisher = {Stuttgart University},
	booktitle = {An Object Representation and Methods for Uncertainty Aware Shape Estimation and Grasping}
}