Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10326747 | Robotics and Autonomous Systems | 2016 | 13 Pages |
Abstract
This paper describes a 3D object perception and perceptual learning system developed for a complex artificial cognitive agent working in a restaurant scenario. This system, developed within the scope of the European project RACE, integrates detection, tracking, learning and recognition of tabletop objects. Interaction capabilities were also developed to enable a human user to take the role of instructor and teach new object categories. Thus, the system learns in an incremental and open-ended way from user-mediated experiences. Based on the analysis of memory requirements for storing both semantic and perceptual data, a dual memory approach, comprising a semantic memory and a perceptual memory, was adopted. The perceptual memory is the central data structure of the described perception and learning system. The goal of this paper is twofold: on one hand, we provide a thorough description of the developed system, starting with motivations, cognitive considerations and architecture design, then providing details on the developed modules, and finally presenting a detailed evaluation of the system; on the other hand, we emphasize the crucial importance of the Point Cloud Library (PCL) for developing such system.1
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Miguel Oliveira, LuÃs Seabra Lopes, Gi Hyun Lim, S. Hamidreza Kasaei, Ana Maria Tomé, Aneesh Chauhan,