2 research outputs found
Robust and fast generation of top and side grasps for unknown objects
In this work, we present a geometry-based grasping algorithm that is capable
of efficiently generating both top and side grasps for unknown objects, using a
single view RGB-D camera, and of selecting the most promising one. We
demonstrate the effectiveness of our approach on a picking scenario on a real
robot platform. Our approach has shown to be more reliable than another recent
geometry-based method considered as baseline [7] in terms of grasp stability,
by increasing the successful grasp attempts by a factor of six.Comment: Extended abstrac
Grasping and Manipulation with a Multi-Fingered Hand
This thesis is concerned with deriving planning algorithms for robot
manipulators. Manipulation has two effects, the robot has a physical effect on
the object, and it also acquires information about the object. This thesis
presents algorithms that treat both problems. First, I present an extension of
the well-known piano mover's problem where a robot pushing an object must plan
its movements as well as those of the object. This requires simultaneous
planning in the joint space of the robot and the configuration space of the
object, in contrast to the original problem which only requires planning in the
latter space. The effects of a robot action on the object configuration are
determined by the non-invertible rigid body mechanics. Second, I consider
planning under uncertainty and in particular planning for information effects.
I consider the case where a robot has to reach and grasp an object under pose
uncertainty caused by shape incompleteness. The approach presented in this
report is to study and possibly extend a new approach to artificial
intelligence (A.I.) which has emerged in the last years in response to the
necessity of building intelligent controllers for agents operating in
unstructured stochastic environments. Such agents require the ability to learn
by interaction with its environment an optimal action-selection behaviour. The
main issue is that real-world problems are usually dynamic and unpredictable.
Thus, the agent needs to update constantly its current image of the world using
its sensors, which provide only a noisy description of the surrounding
environment. Although there are different schools of thinking, with their own
set of techniques, a brand new direction which unifies many A.I. researches is
to formalise such agent/environment interactions as embedded systems with
stochastic dynamics.Comment: Thesis Proposal (RMSG), Technical Report, University of Birmingha