The paper introduces an online user study on applications for social robots with 127 participants. The potential users proposed 570 application scenarios based on the appearance and functionality of four robots presented (AIBO, BARTHOC, BIRON, iCat). The items were grouped into 13 categories which are interpreted and discussed by means of four dimensions: public vs. private use, intensity of interaction, complexity of interaction model, and functional vs. human-like appearance. The interpretation lead to three classes of applications for social robots according to the degree of social interaction: (1) Specialized Applications where the robot has to perform clearly defined tasks which are delegated by a user, (2) Public Applications which are directed to the communication with many users, and (3) Individual Applications with the need of a highly elaborated social model to maintain a variety of situations with few people.

Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to learn both the names and appearances of the objects. Only a small number of local features within any given image are associated with a particular caption word. We describe a connected graph appearance model where vertices represent local features and edges encode spatial relationships. We use the repetition of feature neighborhoods across training images and a measure of correspondence with caption words to guide the search for meaningful feature configurations. We demonstrate improved results on a dataset to which an unstructured object model was previously applied. We also apply the new method to a more challenging collection of captioned images from the Web, detecting and annotating objects within highly cluttered realistic scenes.

Autonomous robots are highly relevant targets for interaction studies, but can exhibit behavioral variability that confounds experimental validity. Currently, testing on real systems is the only means to prevent this, but remains very labour-intensive and often happens too late. To improve this situation, we are working towards early testing by means of partial simulation, with automated assessment, and based upon continuous software integration to prevent regressions. We will introduce the concept and describe a proof-of-concept that demonstrates fast feedback and coherent experiment results across repeated trials.

Mobile robots start to appear in our everyday life, e.g., in shopping malls, airports, nursing homes or warehouses. Often, these robots are operated by non-technical staff with no prior experience/education in robotics. Additionally, as with all new technology, there is certain reservedness when it comes to accepting robots in our personal space. In this work, we propose making use of state-of-the-art Mixed Reality (MR) technology to facilitate acceptance and interaction with mobile robots. By integrating a Microsoft HoloLens into the robot»s operating space, the MR device can be used to a) visualize the robot»s behavior-state and sensor data, b) visually notify the user about planned/future behavior and possible problems/obstacles of the robot, and c) to actively use the device as an additional external sensor source. Moreover, by using the HoloLens, users can operate and interact with the robot without being close to it, as the robot is able to sense with the users» eyes.