4,588 research outputs found
Probabilistic approach to physical object disentangling
Physically disentangling entangled objects from each other is a problem
encountered in waste segregation or in any task that requires disassembly of
structures. Often there are no object models, and, especially with cluttered
irregularly shaped objects, the robot can not create a model of the scene due
to occlusion. One of our key insights is that based on previous sensory input
we are only interested in moving an object out of the disentanglement around
obstacles. That is, we only need to know where the robot can successfully move
in order to plan the disentangling. Due to the uncertainty we integrate
information about blocked movements into a probability map. The map defines the
probability of the robot successfully moving to a specific configuration. Using
as cost the failure probability of a sequence of movements we can then plan and
execute disentangling iteratively. Since our approach circumvents only
previously encountered obstacles, new movements will yield information about
unknown obstacles that block movement until the robot has learned to circumvent
all obstacles and disentangling succeeds. In the experiments, we use a special
probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for
planning and demonstrate successful disentanglement of objects both in 2-D and
3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach
outperforms baseline methods
Probabilistic Approach to Physical Object Disentangling
Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no object models, and especially with cluttered irregularly shaped objects, the robot cannot create a model of the scene due to occlusion. One of our key insights is that based on previous sensory input we are only interested in moving an object out of the disentanglement around obstacles. That is, we only need to know where the robot can successfully move in order to plan the disentangling. Due to the uncertainty we integrate information about blocked movements into a probability map. The map defines the probability of the robot successfully moving to a specific configuration. Using as cost the failure probability of a sequence of movements we can then plan and execute disentangling iteratively. Since our approach circumvents only previously encountered obstacles, new movements will yield information about unknown obstacles that block movement until the robot has learned to circumvent all obstacles and disentangling succeeds. In the experiments, we use a special probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for planning and demonstrate successful disentanglement of objects both in 2-D and 3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach outperforms baseline methods
Learning the Irreducible Representations of Commutative Lie Groups
We present a new probabilistic model of compact commutative Lie groups that
produces invariant-equivariant and disentangled representations of data. To
define the notion of disentangling, we borrow a fundamental principle from
physics that is used to derive the elementary particles of a system from its
symmetries. Our model employs a newfound Bayesian conjugacy relation that
enables fully tractable probabilistic inference over compact commutative Lie
groups -- a class that includes the groups that describe the rotation and
cyclic translation of images. We train the model on pairs of transformed image
patches, and show that the learned invariant representation is highly effective
for classification
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