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Retrieving an object from cluttered spaces suchas cupboards, refrigerators,or bins requires tracking objects with limited or no visual sensing. In thesescenarios, 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. Whileprevious work has focused on multi-target tracking, the assumptions thereinprohibit using prior methods given only the contact-sensing modality. Instead,this paper proposes the method Soft Tracking Using Contacts for ClutteredObjects (STUCCO) that tracks the belief over contact point locations andimplicit object associations using a particle filter. This method allowsambiguous object associations of past contacts to be revised as new informationbecomes available. We apply STUCCO to the Blind Object Retrieval problem, wherea target object of known shape but unknown pose must be retrieved from clutter.Our results suggest that our method outperforms baselines in four simulationenvironments, and on a real robot, where contact sensing is noisy. Insimulation, we achieve grasp success of at least 65% on all environments whileno baselines achieve over 5%.

Soft Tracking Using Contacts for Cluttered Objects to Perform Blind Object Retrieval