  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.
