  Retrieving an object from cluttered spaces suchas cupboards, refrigerators,
or bins requires tracking objects with limited or no visual sensing. In these
scenarios, contact feedback is necessary to estimate the pose of the objects,
yet the objects are movable while their shapes and number may be unknown,
making the association of contacts with objects extremely difficult. While
previous work has focused on multi-target tracking, the assumptions therein
prohibit using prior methods given only the contact-sensing modality. Instead,
this paper proposes the method Soft Tracking Using Contacts for Cluttered
Objects (STUCCO) that tracks the belief over contact point locations and
implicit object associations using a particle filter. This method allows
ambiguous object associations of past contacts to be revised as new information
becomes available. We apply STUCCO to the Blind Object Retrieval problem, where
a target object of known shape but unknown pose must be retrieved from clutter.
Our results suggest that our method outperforms baselines in four simulation
environments, and on a real robot, where contact sensing is noisy. In
simulation, we achieve grasp success of at least 65% on all environments while
no baselines achieve over 5%.
