Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks

A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.Comment: 7 pages, 8 figure

Visual attention and perception in scene understanding for social robotics

Ph.DDOCTOR OF PHILOSOPH

Attention-controlled acquisition of a qualitative scene model for mobile robots

Haasch A. Attention-controlled acquisition of a qualitative scene model for mobile robots. Bielefeld (Germany): Bielefeld University; 2007.Robots that are used to support humans in dangerous environments, e.g., in manufacture facilities, are established for decades. Now, a new generation of service robots is focus of current research and about to be introduced. These intelligent service robots are intended to support humans in everyday life. To achieve a most comfortable human-robot interaction with non-expert users it is, thus, imperative for the acceptance of such robots to provide interaction interfaces that we humans are accustomed to in comparison to human-human communication. Consequently, intuitive modalities like gestures or spontaneous speech are needed to teach the robot previously unknown objects and locations. Then, the robot can be entrusted with tasks like fetch-and-carry orders even without an extensive training of the user. In this context, this dissertation introduces the multimodal Object Attention System which offers a flexible integration of common interaction modalities in combination with state-of-the-art image and speech processing techniques from other research projects. To prove the feasibility of the approach the presented Object Attention System has successfully been integrated in different robotic hardware. In particular, the mobile robot BIRON and the anthropomorphic robot BARTHOC of the Applied Computer Science Group at Bielefeld University. Concluding, the aim of this work, to acquire a qualitative Scene Model by a modular component offering object attention mechanisms, has been successfully achieved as demonstrated on numerous occasions like reviews for the EU-integrated Project COGNIRON or demos

Outdoor view recognition based on landmark grouping and logistic regression

Vision-based robot localization outdoors has remained more elusive than its indoors counterpart. Drastic illumination changes and the scarceness of suitable landmarks are the main difficulties. This paper attempts to surmount them by deviating from the main trend of using local features. Instead, a global descriptor called landmark-view is defined, which aggregates the most visually-salient landmarks present in each scene. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark characterization, based on saliency and color distribution. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. The overall percentage of correct view classification obtained was 80.6%, indicating the adequacy of the approach.Peer ReviewedPostprint (author’s final draft

First Steps Towards an Ethics of Robots and Artificial Intelligence

This article offers an overview of the main first-order ethical questions raised by robots and Artificial Intelligence (RAIs) under five broad rubrics: functionality, inherent significance, rights and responsibilities, side-effects, and threats. The first letter of each rubric taken together conveniently generates the acronym FIRST. Special attention is given to the rubrics of functionality and inherent significance given the centrality of the former and the tendency to neglect the latter in virtue of its somewhat nebulous and contested character. In addition to exploring some illustrative issues arising under each rubric, the article also emphasizes a number of more general themes. These include: the multiplicity of interacting levels on which ethical questions about RAIs arise, the need to recognise that RAIs potentially implicate the full gamut of human values (rather than exclusively or primarily some readily identifiable sub-set of ethical or legal principles), and the need for practically salient ethical reflection on RAIs to be informed by a realistic appreciation of their existing and foreseeable capacities

Efficient resource allocation for automotive active vision systems

Individual mobility on roads has a noticeable impact upon peoples' lives, including traffic accidents resulting in severe, or even lethal injuries. Therefore the main goal when operating a vehicle is to safely participate in road-traffic while minimising the adverse effects on our environment. This goal is pursued by road safety measures ranging from safety-oriented road design to driver assistance systems. The latter require exteroceptive sensors to acquire information about the vehicle's current environment. In this thesis an efficient resource allocation for automotive vision systems is proposed. The notion of allocating resources implies the presence of processes that observe the whole environment and that are able to effeciently direct attentive processes. Directing attention constitutes a decision making process dependent upon the environment it operates in, the goal it pursues, and the sensor resources and computational resources it allocates. The sensor resources considered in this thesis are a subset of the multi-modal sensor system on a test vehicle provided by Audi AG, which is also used to evaluate our proposed resource allocation system. This thesis presents an original contribution in three respects. First, a system architecture designed to efficiently allocate both high-resolution sensor resources and computational expensive processes based upon low-resolution sensor data is proposed. Second, a novel method to estimate 3-D range motion, e cient scan-patterns for spin image based classifiers, and an evaluation of track-to-track fusion algorithms present contributions in the field of data processing methods. Third, a Pareto efficient multi-objective resource allocation method is formalised, implemented, and evaluated using road traffic test sequences

Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes

In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods. The proposed solution exploits salient features present in a larger field of view, but instead of employing active vision we replace the cameras with stereo rigs featuring a long focal analysis camera, as well as a short focal registration camera. Thus, we are able to propose an accurate solution which does not require intrinsic variation models as in the case of zooming cameras. Moreover, the availability of the two views simultaneously in each rig allows for pose re-estimation between rigs as often as necessary. The algorithm has been successfully validated in an indoor setting, as well as on a difficult scene featuring a highly dense pilgrim crowd in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application