This paper introduces a novel robot-based approach to the stroke rehabilitation scenario, in which a mobile robot companion accompanies stroke patients during their walking self-training. This assistance enables them to move freely in the clinic practicing both their mobility and spatial orientation skills. Based on a set of questions for systematic evaluating the autonomy and practicability of assistive robots and a three-stage approach in conducting function and user tests in the clinical setting, we present the results of user trials performed with N=30 stroke patients in a stroke rehabilitation center between 4/2015 and 3/2016. This allowed us to make an honest inventory of the strengths and weaknesses of the developed robot companion and its already achieved practicability for clinical use. The results of the user studies show that patients and fellow patients were very open-minded and accepted the robotic coach. The robot motivated them for independent training and leaving their room, despite severe consequences of stroke (lower limbs paralysis, speech/language problems, loss of orientation, depression), provided a very self-determined training regime, and encouraged them to expand the radius of their training in the clinic.

As report on work in progress, this paper describes the objectives and the current state of implementation of the ongoing research project ROREAS (Robotic Rehabilitation Assistant for Stroke Patients), which aims at developing a robotic rehabilitation assistant for walking and orientation exercising in self-training during clinical stroke follow-up care. This requires strongly user-centered, polite and attentive social navigation and interaction behaviors that can motivate the patients to start, continue, and regularly repeat their self-training. Against this background, the paper gives an overview of the constraints and requirements arising from the rehabilitation scenario and the operational environment, a heavily populated multi-level rehabilitation center, and presents the robot platform ROREAS which is currently used for developing the demonstrators (walking coach and orientation coach). Moreover, it gives an overview of the robot's functional system architecture and presents selected advanced navigation and HRI functionalities required for a personal robotic trainer that can successfully operate in such a challenging real-world environment, up to the results of ongoing functionality tests and upcoming user studies.

In this paper, we present MIRA, a new middleware for robotic applications. It is designed for use in real-world applications and for research and teaching. In comparison to many other existing middlewares, MIRA employs novel techniques for communication that are described in this paper. Moreover, we present benchmarks that analyze the performance of the most commonly used middlewares ROS, Yarp, LCM, Player, Urbi, and MOOS. Using these benchmarks, we can show that MIRA outperforms the other middlewares in terms of latency and computation time.

The paper is addressing several aspects of our work as part of the European FP7 project ¿CompanionAble¿ and gives an overview of the progress in developing a socially assistive home robot companion for elderly people with mild cognitive impairment (MCI) living alone at home. The spectrum of required assistive functionalities and services that have been specified by the different end-user target groups of such a robot companion (the elderly, relatives, caregivers) is manifold. It reaches from situation-specific, intelligent reminding (e.g. taking medication or drinking) and cognitive stimulation, via mobile videophony with relatives or caregivers, up to the autonomous detection of dangerous situations, like falls, and their evaluation by authorized persons via mobile telepresence. From the beginning, our approach has been focused on long-term and everyday suitability and low-cost producibility as important prerequisites for the marketability of the robot companion. Against this background, the paper presents the main system requirements derived from user studies, the consequences for the hardware design and functionality of the robot companion, its system architecture, a key technology for HRI in home environments - the autonomous user tracking and searching, up to the results of already conducted and ongoing functionality tests and upcoming user studies.

The paper gives an overview of the progress in developing a socially assistive home robot companion for elderly people with mild cognitive impairment (MCI) living alone at home. The spectrum of required assistive functionalities of such a robot companion is broad and reaches from reminding functions (e.g. taking medication or drinking) and cognitive stimulation exercises, via mobile videophony with relatives or caregivers, up to the detection and evaluation of critical situations, like falls. The paper is addressing several aspects of our work as part of the European FP7 project “CompanionAble”, as for example the developed robot hardware and its software and control architecture, the implemented skills for robust user detection and tracking and user-centered navigation in the home environment, and reports on already conducted and still ongoing functionality testings and pending usability studies with the end-user target groups (the elderly, relatives, caregivers).

The paper gives a comprehensive overview of our Shopping Guide project, which aims at the development of interactive mobile shopping companion robots for everyday use in challenging operating environments such as home improvement stores. It is spanning an arc from the expectations and requirements of store owners and customers, via the challenges of the shopping scenario and the operating environment, the implemented functionality of the shopping guide robots, up to the results of long-term field trials. The field trials started in April 2008 and still ongoing aim at studying whether and how a group of interactive mobile shopping guide robots can operate completely autonomously in such everyday environments and how they are accepted by uninstructed customers. In these field trials, where nine robotic shopping guides traveled together 2187 kilometers in three different home improvement stores in Germany, more than 8,600 customers were successfully guided to the locations of their products of choice. With the successful development of these shopping guide robots, a further important step towards assistive robotics for daily use has been done.

In this paper, we present an approach for facial expression classification, based on Active Appearance Models. To be able to work in real-world, we applied the AAM framework on edge images, instead of gray images. This yields to more robustness against varying lighting conditions. Additionally, three different facial expression classifiers (AAM classifier set, MLP and SVM) are compared with each other. An essential advantage of the developed system is, that it is able to work in real-time - a prerequisite for the envisaged implementation on an interactive social robot. The real-time capability was achieved by a two-stage hierarchical AAM tracker and a very efficient implementation.

The paper describes progress achieved in our long-term research project ShopBot, which aims at the development of an intelligent and interactive mobile shopping assistant for everyday use in shopping centers or home improvement stores. It is focusing on recent progress concerning two important methodological aspects: (i) the on-line building of maps of the operation area by means of advanced Rao-Blackwellized SLAM approaches using both sonar-based gridmaps as well as vision-based graph maps as representations, and (ii) a probabilistic approach to multi-modal user detection and tracking during the guidance tour. Experimental results of both the map building characteristics and the person tracking behavior achieved in an ordinary home improvement store demonstrate the reliability of both approaches. Moreover, we present first very encouraging results of long-term field trials which have been executed with three robotic shopping assistants in another home improvement store in Bavaria since March 2008. In this field test, the robots could demonstrate their suitability for this challenging real-world application, as well as the necessary user acceptance.

In this paper, we present two important aspects of our human-robot communication interface which is being developed in the context of our long-term research framework PERSES dealing with the development of highly interactive mobile robotic assistants. First, we introduce a multi-modal people detection and tracking system, a fundamental prerequisite for the observation of a human interaction partner and his nonverbal instructions given by pointing poses, gestures, head pose and eye gaze. Based on this detection and tracking system, we present a hierarchical neural architecture that is capable of estimating a target point at the floor given a pointing pose, thus enabling a user to command his mobile robot to a specific target position in his local surroundings by means of pointing. In this context, we were especially interested in determining whether it is possible to accomplish such a target point estimator using only monocular images of low-cost cameras. Both the tracker and the target point estimator were implemented and experimentally investigated on our mobile robotic assistant HOROS. The achieved recognition results presented finally demonstrate that it is in fact possible to realize a user-independent pointing pose estimation using monocular images only, but further efforts are necessary to improve the robustness of this approach for everyday application.

In this paper, we present a neural architecture that is capable of estimating a target point from a pointing gesture, thus enabling a user to command a mobile robot to a specific position in his local surroundings by means of pointing. In this context, we were especially interested to determine whether it is possible to implement a target point estimator using only monocular images of low-cost Webcams. The feature extraction is also quite straightforward: We use a gabor jet to extract the feature vector from the normalized camera images; and a cascade of multi layer perceptron (MLP) classifiers as estimator. The system was implemented and tested on our mobile robotic assistant HOROS. The results indicate that it is in fact possible to realize a pointing estimator using monocular image data, but further efforts are necessary to improve the accuracy and robustness of our approach

For socially interactive robots it is essential to be able to estimate the interest of people to interact with them. Based on this estimation the robot can adapt its dialog strategy to the different people's behaviors. Consequently, efficient and robust techniques for people detection and tracking are basic prerequisites when dealing with human-robot interaction (HRI) in real-world scenarios. In this paper, we introduce an imposed approach for integration of several sensor modalities and present a multimodal, probability-based people detection and tracking system and its application using the different sensory systems of our mobile interaction robot HOROS. For each of these sensory cues, separate and specific Gaussian distributed hypotheses are generated and further merged into a robot-centered map by means of a flexible probabilistic aggregation scheme based on covariance intersection (CI). The main advantages of this approach are the simple extensibility by integration of further sensory channels, even with different update frequencies, and the usability in real-world HRI tasks. Finally, promising experimental results achieved for people tracking in a real-world environment, and university building, are presented

This paper presents a real-time algorithm for acquiring compact three-dimensional maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on real-time pose estimation during mapping, our approach extends the popular expectation-maximization algorithm to multisurface models, and makes it amenable to real-time execution. Maps acquired by our algorithm consist of compact sets of textured polygons that can be visualized interactively. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real time and in a fully automated fashion.

Describes an online algorithm for generating compact 3D maps of mobile robot environments. Maps generated by our approach consist of small numbers of planar surfaces, which are augmented by fine-grained polygons for non-flat environmental features. Our approach builds on the expectation maximization (EM) algorithm, but develops a new, incremental version that can be executed in real-time. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real-time, from range and camera data collected by a mobile robot.