Inhalt des Dokuments
Dov
Katz [1]
INTERACTIVE PERCEPTION OF ARTICULATED OBJECTS FOR
AUTONOMOUS MANIPULATION
[2]
SEPTEMBER 2011
B.Sc., TEL AVIV UNIVERSITY, ISRAEL
M.Sc., UNIVERSITY OF
MASSACHUSETTS AMHERST
Ph.D., UNIVERSITY OF MASSACHUSETTS
AMHERST
Directed by: Professor Oliver Brock
This thesis develops robotic skills for manipulating novel
articulated objects. The degrees of freedom of an articulated object
describe the relationship
among its rigid bodies, and are often
relevant to the object's intended
function. Examples of everyday
articulated objects include scissors, pliers, doors, door handles,
books, and drawers. Autonomous manipulation of
articulated
objects is therefore a prerequisite for many robotic applications in
our everyday environments.
Already today, robots perform complex
manipulation tasks, with impressive accuracy and speed, in controlled
environments such as factory oors. An important characteristic of
these environments is that they can be engineered to reduce or even
eliminate perception. In contrast, in unstructured environments such
as our homes and offices, perception is typically much more
challenging. Indeed, manipulation in these unstructured environments
remains largely unsolved. We therefore assume that to enable
autonomous manipulation of objects in our everyday environments,
robots must be able to acquire information about these objects, making
as few assumption about the environment as possible.
Acquiring
information about the world from sensor data is a challenging problem.
Because there is so much information that could be measured about the
environment, considering all of it is impractical given current
computational speeds. Instead, we propose to leverage our
understanding of the task, in order to determine the relevant
information. In our case, this information consists of the object's
shape and kinematic structure. Perceiving this task-specic
information is still challenging. This is because in order to
understand the object's degrees of freedom, we must observe relative
motion between its rigid bodies. And, as relative motion is not
guaranteed to occur, this information may not be included in the
sensor stream.
The main contribution of this thesis is the design
and implementation of a robotic system capable of perceiving and
manipulating articulated objects. This system relies on Interactive
Perception, an approach which exploits the synergies that arise when
crossing the boundary between action and perception. In interactive
perception, the emphasis of perception shifts from object appearance
to object function. To enable the perception and manipulation of
articulated objects, this thesis develops algorithms for perceiving
the kinematic structure and shape of objects. The resulting perceptual
capabilities are used within a relational reinforcement learning
framework, enabling a robot to obtain general domain knowledge for
manipulation. This composition enables our robot to reliably and
efficiently manipulate novel articulated objects. To verify the
eectiveness of the proposed robotic system, simulated and real-world
experiments were conducted with a variety of everyday objects.
Advisor: Oliver Brock [3]
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