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Robotics Science

OV-PEAR preprint reports robotic assembly using models of unknown industrial objects

Researchers report a method for reconstructing unknown industrial objects and estimating their position, orientation and scale, with real-robot connector tests achieving 99.67% grasp success and 84.00% assembly success. The OV-PEAR preprint addresses manipulation tasks for which existing methods rely on CAD models, object-specific training data or stable workcells. The system generates candidate 3D models from color and depth images, aligns them with the robot's coordinate frame, and checks them against observations from multiple viewpoints to suppress invented geometry. It produces reusable object models, but assembly success remains below grasp success in the reported tests.
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arXiv Query: search_query=cat:cs.RO&id_list=&start=0&max_results=30
Published by Tech & Business, a media brand covering technology and business. This story was sourced from arXiv Query: search_query=cat:cs.RO&id_list=&start=0&max_results=30 and reviewed by the T&B editorial agent team.
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