712 research outputs found
Learning to Place New Objects
The ability to place objects in the environment is an important skill for a
personal robot. An object should not only be placed stably, but should also be
placed in its preferred location/orientation. For instance, a plate is
preferred to be inserted vertically into the slot of a dish-rack as compared to
be placed horizontally in it. Unstructured environments such as homes have a
large variety of object types as well as of placing areas. Therefore our
algorithms should be able to handle placing new object types and new placing
areas. These reasons make placing a challenging manipulation task. In this
work, we propose a supervised learning algorithm for finding good placements
given the point-clouds of the object and the placing area. It learns to combine
the features that capture support, stability and preferred placements using a
shared sparsity structure in the parameters. Even when neither the object nor
the placing area is seen previously in the training set, our algorithm predicts
good placements. In extensive experiments, our method enables the robot to
stably place several new objects in several new placing areas with 98%
success-rate; and it placed the objects in their preferred placements in 92% of
the cases
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
A Caging Inspired Gripper using Flexible Fingers and a Movable Palm
This paper proposes the design of a robotic
gripper motivated by the bin-picking problem, where a variety
of objects need to be picked from cluttered bins. The presented
gripper design focuses on an enveloping cage-like approach,
which surrounds the object with three hooked fingers, and
then presses into the object with a movable palm. The fingers
are flexible and imbue grasps with some elasticity, helping to
conform to objects and, crucially, adding friction to cases where
an object cannot be caged. This approach proved effective on
a set of basic shapes, such as cuboids and cylinders, in which
every object could be grasped. In particular, flat bottom parts
could be grasped in a very stable manner, as demonstrated by
testing grasps with multiple 5N and 10N disturbances. A set
of supermarket items were also tested, highlighting promising
features such as effective grasping of fruits and vegetables, as
well as some limitations in the current embodiment, which is
not always able to slip the fingers underneath objects
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