Help me help you: Interfaces for personal robots

Index Terms-HRI, mobile user interface, information theory I. RESEARCH PROBLEM AND A PROPOSAL The communication bottleneck between robots and people People are adept at compensating for communication limitations, changing their communicative strategies for talking to pets, babies We propose to approach this problem by accounting for limitations in robot abilities and taking advantage of already familiar human-computer interaction models, leveraging a communication model based upon Information Theory. Using this design perspective, we present three different mobile user interfaces that were fully developed and implemented on a PR2 (Personal Robot 2) [6] for task domains in navigation, perception, learning and manipulation. II. RELEVANT THEORIES We can observe parallels between human robot interaction and the interaction between humans and general complex autonomous systems. Sheridan's taxonomy of complex human-machine systems describes the following sequence of operations: (1) acquire information, (2) analyze and display information, (3) decide on an action, and (4) implement that action [7, p. 61]. This provides the groundwork for identifying the stages at which people and/or robots should lead. In the current projects, the personal robot autonomously completes steps 1, 2 and 4, and the person completes step 3. Thus, the user interface design must address how the robot analyzes and displays its sensor information and world model to the human, and how the human can effectively communicate desired actions to the robot. An analysis of our case studies in Sheridan's framework is displayed in Gold proposed using an Information Pipeline model for HRI that is based upon information theory [8], a mathematical model of communication developed for quantifying the amount of information that could be transported through a given channel. Schramm [9] developed a theory of communication that put these ideas into the context of two-way joint communications. This could be helpful when considering the large amount of overhead involved in encoding and decoding messages sent between people and robots. The focus of the projects in this paper was on designing interfaces that applied this theory to human-robot communication. With a robot encoding messages in a way that humans can understand and humans encoding messages in a way that robots can understand, communication is easy and effective. III. THE DESIGN SPACE AND THREE UIS The personal robot platform used throughout these projects is the PR2, and the robot behaviors are built using the Robot Operating System (ROS

Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives

The importance of depth perception in the interactions that humans have within their nearby space is a well established fact. Consequently, it is also well known that the possibility of exploiting good stereo information would ease and, in many cases, enable, a large variety of attentional and interactive behaviors on humanoid robotic platforms. However, the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras often prevents from relying on this kind of cue to visually guide robots' attention and actions in real-world scenarios. The contribution of this paper is two-fold: first, we show that the Efficient Large-scale Stereo Matching algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map is well suited to be used on a humanoid robotic platform as the iCub robot; second, we show how, provided with a fast and reliable stereo system, implementing relatively challenging visual behaviors in natural settings can require much less effort. As a case of study we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. Indeed this example paves the way to a variety of other similar applications

Deep Detection of People and their Mobility Aids for a Hospital Robot

Robots operating in populated environments encounter many different types of people, some of whom might have an advanced need for cautious interaction, because of physical impairments or their advanced age. Robots therefore need to recognize such advanced demands to provide appropriate assistance, guidance or other forms of support. In this paper, we propose a depth-based perception pipeline that estimates the position and velocity of people in the environment and categorizes them according to the mobility aids they use: pedestrian, person in wheelchair, person in a wheelchair with a person pushing them, person with crutches and person using a walker. We present a fast region proposal method that feeds a Region-based Convolutional Network (Fast R-CNN). With this, we speed up the object detection process by a factor of seven compared to a dense sliding window approach. We furthermore propose a probabilistic position, velocity and class estimator to smooth the CNN's detections and account for occlusions and misclassifications. In addition, we introduce a new hospital dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm that our pipeline successfully keeps track of people and their mobility aids, even in challenging situations with multiple people from different categories and frequent occlusions. Videos of our experiments and the dataset are available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAidsComment: 7 pages, ECMR 2017, dataset and videos: http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids

Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, FPFH features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

This Far, No Further: Introducing Virtual Borders to Mobile Robots Using a Laser Pointer

We address the problem of controlling the workspace of a 3-DoF mobile robot. In a human-robot shared space, robots should navigate in a human-acceptable way according to the users' demands. For this purpose, we employ virtual borders, that are non-physical borders, to allow a user the restriction of the robot's workspace. To this end, we propose an interaction method based on a laser pointer to intuitively define virtual borders. This interaction method uses a previously developed framework based on robot guidance to change the robot's navigational behavior. Furthermore, we extend this framework to increase the flexibility by considering different types of virtual borders, i.e. polygons and curves separating an area. We evaluated our method with 15 non-expert users concerning correctness, accuracy and teaching time. The experimental results revealed a high accuracy and linear teaching time with respect to the border length while correctly incorporating the borders into the robot's navigational map. Finally, our user study showed that non-expert users can employ our interaction method.Comment: Accepted at 2019 Third IEEE International Conference on Robotic Computing (IRC), supplementary video: https://youtu.be/lKsGp8xtyI