21,901 research outputs found
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Integrating mobile robotics and vision with undergraduate computer science
This paper describes the integration of robotics education into an undergraduate Computer Science curriculum. The proposed approach delivers mobile robotics as well as covering the closely related field of Computer Vision, and is directly linked to the research conducted at the authorsā institution. The paper describes the most relevant details of the module content and assessment strategy, paying particular attention to the practical sessions using Rovio mobile robots. The specific choices are discussed that were made with regard to the mobile platform, software libraries and lab environment. The paper also presents a detailed qualitative and quantitative analysis of student results, including the correlation between student engagement and performance, and discusses the outcomes of this experience
Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics
Developmental robotics is an emerging field located
at the intersection of developmental psychology
and robotics, that has lately attracted
quite some attention. This paper gives a survey of
a variety of research projects dealing with or inspired
by developmental issues, and outlines possible
future directions
Real-time Convolutional Neural Networks for Emotion and Gender Classification
In this paper we propose an implement a general convolutional neural network
(CNN) building framework for designing real-time CNNs. We validate our models
by creating a real-time vision system which accomplishes the tasks of face
detection, gender classification and emotion classification simultaneously in
one blended step using our proposed CNN architecture. After presenting the
details of the training procedure setup we proceed to evaluate on standard
benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66%
in the FER-2013 emotion dataset. Along with this we also introduced the very
recent real-time enabled guided back-propagation visualization technique.
Guided back-propagation uncovers the dynamics of the weight changes and
evaluates the learned features. We argue that the careful implementation of
modern CNN architectures, the use of the current regularization methods and the
visualization of previously hidden features are necessary in order to reduce
the gap between slow performances and real-time architectures. Our system has
been validated by its deployment on a Care-O-bot 3 robot used during
RoboCup@Home competitions. All our code, demos and pre-trained architectures
have been released under an open-source license in our public repository.Comment: Submitted to ICRA 201
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