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In this paper we present IREP, an Interactive Robotics Education Program that aims to integrate basic concepts of mechatronics, electronics and computer science for undergraduate engineering students in an interactive manner. Our methodology is inspired in the process in which project management techniques are used in order to define goals, milestone planning is used to guide the implementation, scheduling techniques are used to monitor project advances, and resource management guidelines are used to assign human and monetary resources. Initially, we present a case of study that describes the current state of the robotics program at the Pontifical Catholic University of Peru, then we identify key problems using surveys with current undergraduate students, and finally we propose potential solutions to each problem and integrate them into a new comprehensive education program. Using our new approach, students will enhance their hands-on experience with real robotics projects while performing preliminary research, documentation and oral presentations that will help them to develop their intellectual and communication skills. [ABSTRACT FROM PUBLISHER]

This paper presents a software interface that allows a user to control different types of robotic systems by using a Brain-Machine Interface. Unlike common device-specific BMI systems, our software architecture maps simple EEG-based commands to diverse functionalities depending on the robotic platform, so the user does not have learn to generate new EEG commands for different robots. The graphic user interface provides a mechanism that allows the user to navigate through menus using EMG signals (i.e. eye-blink), and then execute robot commands using EEG signals. Our software is based on a modular design that allows the integration of new robotic platforms with easy customization. Our current prototype explores the controllability of a humanoid robot, a flying robot and a pan-tilt robot using the proposed software interface. Experimental evidence shows that the system achieves good user satisfaction as well as easy controllability of different types of robotic systems. [ABSTRACT FROM PUBLISHER]

Social imitation learning is an essential skill that humans use to achieve social acceptance, increase awareness in unknown situations or to achieve cultural adaptation. In this work we address the problem of social imitation learning in a many-to-one learning scheme (group of humans to robot), where humans do not necessarily have teaching roles. Contrary to common imitation learning approaches based on one-to-one learning schemes with two agents (human teacher and robot student), our approach is inspired by social learning theory and consists in performing human behavior modeling by observing multiple humans while discovering common behavioral patterns. We propose a common framework for social behavior feature extraction that can be used to collect essential information of various social behaviors such as multi-person trajectory and multiple-body pose. Considering the fact that social imitation learning is shaped by stimuli of others' behavior and the more individuals define the behavior, the more likely to engage in it; our modeling approach also considers a social force model that triggers social behavior learning when observing a group of people. Finally, collective behavior modeling is achieved by feature clustering using a Gaussian Mixture Model approach. Experimental results show that our approach is suitable for social human behavior modeling in situations such as emergency evacuation and Japanese style greeting (bowing). [ABSTRACT FROM PUBLISHER]

Pattern detection algorithms may be used as part of safety-relevant processes employed by industrial systems. Current approaches to functional safety mainly focus on random faults in hardware and the avoidance of systematic faults in both software and hardware. In this paper we build on the concepts of the international standard for functional safety IEC 61508 to extend safety-relevant notions to numerical and logical processes (algorithms) employed in pattern detection systems. In particular, we target the uncertainty pertaining to face detection systems where incorrect detection affects the overall system performance. We discuss a dual channel system that comprises two of the most commonly used and widely available face detection algorithms, Viola-Jones and Kienzle et al. We present a method for deriving the probability of failure in demand (PFD) from the combination of these two channels using both: 1oo2 and 2oo2 voting schemes. Finally, we compare experimental results from both the perspectives of availability and safety, and present conclusions with respect to the appropriate choice of information combination schemes and system architectures. [ABSTRACT FROM PUBLISHER]

In this paper we present our semi-supervised technique for building object category classifiers using real image data from the Internet. Our technique not only reduces the overhead of manual training by humans, but also achieves robust classifiers that can be evaluated in real time. Given a sample object and its name (keyword), we collect a large amount of object-related images from two main image sources: Google Images and the LabelMe website. We deal with the problem of separating good training samples from noisy images by performing two steps: similar image selection and non-real image filtering. We use a variant of Gaussian Discriminant Analysis (GDA) to filter out non-real images (drawings, cartoons, etc.) that tentatively affect classifier performance in real environments. In order to select true object-related training samples, we introduce a Simile Selector Classifier (SSC) that is constructed from a small set of images taken from the sample object. The SSC not only is able to select similar samples from the large unordered set of images, but also it can separate desired object category images from other categories that have the same name (polysemes), i.e. “apple” as a fruit, or as a company logo. Finally, the experiments which we performed in real environments demonstrate the performance of our object classifiers. [ABSTRACT FROM PUBLISHER]

This paper seeks to establish a precedent for future development and design of social robots by considering the knowledge and expectations about robots of a group of 296 children. Human-robot interaction experiments were con- ducted with a Teleoperated anthropomorphic robot, and surveys were taken before and after the experiments. Children were also asked to perform a drawing of a robot. An image analysis algorithm was developed to classify drawings into 4 types: Anthropomorphic Mechanic/Non Mechanic (AM/AnM) and Non-Anthropomorphic Mechanic/Non Mechanic (nAM/nAnM). Image analysis algorithm was used in combination with human classification using a 2oo3 (two out of three) voting scheme to find children's strongest stereo- type about robots. Survey and image analysis results suggest that children in general have some general knowledge about robots, and some children even have a deep under- standing and expectations for future robots. Moreover, children's strongest stereotype is directed towards mechanical anthropomorphic systems. [ABSTRACT FROM AUTHOR]

This paper seeks to establish a precedent for future development and design of social robots by considering the knowledge and expectations about robots of a group of 296 children. Humanrobot interaction experiments were conducted with a Teleoperated anthropomorphic robot, and surveys were taken before and after the experiments. Children were also asked to perform a drawing of a robot. An image analysis algorithm was developed to classify drawings into 4 types: Anthropomorphic Mechanic/Non Mechanic (AM/AnM) and Non-Anthropomorphic Mechanic/Non Mechanic (nAM/nAnM). Image analysis algorithm was used in combination with human classification using a 2003 (two out of three) voting scheme to find children's strongest stereo-type about robots. Survey and image analysis results suggest that children in general have some general knowledge about robots, and some children even have a deep understanding and expectations for future robots. Moreover, children's strongest stereotype is directed towards mechanical anthropomorphic systems. [ABSTRACT FROM PUBLISHER]

We present our method for learning object categories from the internet using cues obtained through human-robot interaction. Such cues include an object model acquired by observation and the name of the object. Our learning approach emulates the natural learning process of children when they observe their environment, encounter unknown objects and ask adults the name of the object. Using this learning approach, our robot is able to discover objects in a domestic environment by observing when humans naturally move objects as part of their daily activities. Using speech interface,the robot directly asks humans the name of the object by showing an example of the acquired model. The name in text format and the previously learnt model serve as input parameters to retrieve object category images from a search engine, select similar object images, and build a classifier. Preliminary results demonstrate the effectiveness of our learning approach. [ABSTRACT FROM AUTHOR]