Proceedings ArticleDOI
Interactive teaching and experience extraction for learning about objects and robot activities
Gi Hyun Lim,Miguel Oliveira,Vahid Mokhtari,S. Hamidreza Kasaei,Aneesh Chauhan,Luís Seabra Lopes,Ana Maria Tomé +6 more
- pp 153-160
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TLDR
The robot's ontology is extended with concepts for representing human-robot interactions as well as the experiences of the robot, and these experiences are extracted and stored in memory and they are used as input for learning methods.Abstract:
Intelligent service robots should be able to improve their knowledge from accumulated experiences through continuous interaction with the environment, and in particular with humans. A human user may guide the process of experience acquisition, teaching new concepts, or correcting insufficient or erroneous concepts through interaction. This paper reports on work towards interactive learning of objects and robot activities in an incremental and open-ended way. In particular, this paper addresses human-robot interaction and experience gathering. The robot's ontology is extended with concepts for representing human-robot interactions as well as the experiences of the robot. The human-robot interaction ontology includes not only instructor teaching activities but also robot activities to support appropriate feedback from the robot. Two simplified interfaces are implemented for the different types of instructions including the teach instruction, which triggers the robot to extract experiences. These experiences, both in the robot activity domain and in the perceptual domain, are extracted and stored in memory, and they are used as input for learning methods. The functionalities described above are completely integrated in a robot architecture, and are demonstrated in a PR2 robot.read more
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Journal ArticleDOI
Towards lifelong assistive robotics: A tight coupling between object perception and manipulation
TL;DR: Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks.
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3D object perception and perceptual learning in the RACE project
Miguel Oliveira,Luís Seabra Lopes,Gi Hyun Lim,S. Hamidreza Kasaei,Ana Maria Tomé,Aneesh Chauhan +5 more
TL;DR: An object perception and perceptual learning system developed for a complex artificial cognitive agent working in a restaurant scenario that integrates detection, tracking, learning and recognition of tabletop objects and the Point Cloud Library is used in nearly all modules.
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Interactive Open-Ended Learning for 3D Object Recognition: An Approach and Experiments
TL;DR: An efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner, which is able to interact with human users, learning new object categories continuously over time is presented.
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The RACE Project : Robustness by Autonomous Competence Enhancement
Joachim Hertzberg,Jianwei Zhang,Liwei Zhang,Sebastian Rockel,Bernd Neumann,Jos Lehmann,Krishna Dubba,Anthony G. Cohn,Alessandro Saffiotti,Federico Pecora,Masoumeh Mansouri,Štefan Konečný,Martin Günther,Sebastian Stock,Luís Seabra Lopes,Miguel Oliveira,Gi Hyun Lim,Hamidreza Kasaei,Vahid Mokhtari,Lothar Hotz,Wilfried Bohlken +20 more
TL;DR: The general system architecture is introduced and some results in detail regarding hybrid reasoning and planning used in RACE are sketches, and instances of learning from the experiences of real robot task execution are sketched.
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Ego-centered networks and the ripple effect
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