Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164360
Title: Template-based category-agnostic instance detection for robotic manipulation
Authors: Hu, Zhongxu
Tan, Runjia
Zhou, Yanxin
Woon, Junyang
Lv, Chen
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Hu, Z., Tan, R., Zhou, Y., Woon, J. & Lv, C. (2022). Template-based category-agnostic instance detection for robotic manipulation. IEEE Robotics and Automation Letters, 7(4), 12451-12458. https://dx.doi.org/10.1109/LRA.2022.3219021
Journal: IEEE Robotics and Automation Letters
Abstract: An intelligent robotic system is one of the key pillars of a smart factory that requires flexibility to handle a variety of tasks. Perception is a key enabling technology for robots. Most existing object detection studies have mainly focused on category-specific objects and have achieved impressive performance. However, robotic systems, particularly in industrial scenarios, typically interact with many category-agnostic objects, which the robot must detect instantly without pre-training. Therefore, in this study, we proposed a template-based detection and segmentation approach, which incorporated a multi-level correlation model and a similarity-refine module, for handling the category-agnostic instance. The proposed approach was then validated and demonstrated in an interactive and adaptive robotic application scenario designed for the typical pick-and-place task. Among them, the picking scan path and location were instructed through human guidance with hand tracking. The neural rendering technology was also introduced to render novel views of the template. The proposed approach was evaluated using a benchmark and verified through a real demonstration.
URI: https://hdl.handle.net/10356/164360
ISSN: 2377-3766
DOI: 10.1109/LRA.2022.3219021
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/LRA.2022.3219021.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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