A Multimodal Dataset for Object Model Learning from Natural Human-Robot Interaction
Financiación FP7 / Fp7 Funds
Resumen: Learning object models in the wild from natural human interactions is an essential ability for robots to per- form general tasks. In this paper we present a robocentric multimodal dataset addressing this key challenge. Our dataset focuses on interactions where the user teaches new objects to the robot in various ways. It contains synchronized recordings of visual (3 cameras) and audio data which provide a challenging evaluation framework for different tasks.
Additionally, we present an end-to-end system that learns object models using object patches extracted from the recorded natural interactions. Our proposed pipeline follows these steps: (a) recognizing the interaction type, (b) detecting the object that the interaction is focusing on, and (c) learning the models from the extracted data. Our main contribution lies in the steps towards identifying the target object patches of the images. We demonstrate the advantages of combining language and visual features for the interaction recognition and use multiple views to improve the object modelling.
Our experimental results show that our dataset is challenging due to occlusions and domain change with respect to typical object learning frameworks. The performance of common out- of-the-box classifiers trained on our data is low. We demonstrate that our algorithm outperforms such baselines.

Idioma: Inglés
Año: 2017
Publicado en: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2017), [8 pp.]
ISSN: 2153-0858

Originalmente disponible en: Texto completo de la revista

Financiación: info:eu-repo/grantAgreement/EUR/FP7/3rdHand-610878
Financiación: info:eu-repo/grantAgreement/ES/MINECO/PCIN-2015-122
Tipo y forma: Article (PostPrint)
Área (Departamento): Ingeniería de Sistemas y Automática (Departamento de Informática e Ingeniería de Sistemas)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


Exportado de SIDERAL (2018-05-31-09:49:03)

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Articles > Artículos por área > Ingeniería de Sistemas y Automática



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