Soft Tracking Using Contacts for Cluttered Objects to Perform Blind Object Retrieval
Sheng Zhong, Nima Fazeli, and Dmitry Berenson

TL;DR
This paper introduces STUCCO, a particle filter-based method for tracking objects in cluttered spaces using contact feedback alone, enabling blind object retrieval despite noisy sensing and ambiguous object associations.
Contribution
The paper presents a novel contact-based tracking method that revises object associations over time, improving retrieval success in cluttered environments with limited sensing.
Findings
Achieves at least 65% grasp success in simulations
Outperforms baselines which achieve under 5% success
Effective on real robot with noisy contact sensing
Abstract
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Domain Adaptation and Few-Shot Learning · Advanced Neural Network Applications
