1,721,074 research outputs found
Analysis of human-robot spatial behaviour applying a qualitative trajectory calculus
No full textThe analysis and understanding of human-robot joint spatial behaviour (JSB) - such as guiding, approaching, departing, or coordinating movements in narrow spaces - and its communicative and dynamic aspects are key requirements on the road towards more intuitive interaction, safe encounter, and appealing living with mobile robots. This endeavours demand for appropriate models and methodologies to represent JSB and facilitate its analysis. In this paper, we adopt a qualitative trajectory calculus (QTC) as a formal foundation for the analysis and representation of such spatial behaviour of a human and a robot based on a compact encoding of the relative trajectories of two interacting agents in a sequential model. We present this QTC together with a distance measure and a probabilistic behaviour model and outline its usage in an actual JSB study. We argue that the proposed QTC coding scheme and derived methodologies for analysis and modelling are flexible and extensible to be adapted for a variety of other scenarios and studies. © 2012 IEEE
A probabilistic model of human-robot spatial interaction using a qualitative trajectory calculus
No full textMoving from augmented to interactive mapping
No full textBooij O, Kröse B, Peltason J, Spexard T, Hanheide M. Moving from augmented to interactive mapping. In: Robotics: Science and Systems Conference. 2008
ToBI - Team of Bielefeld: The Human-Robot Interaction System for RoboCup@Home 2009
Full text linkWachsmuth S, Hanheide M, Siepmann F, Spexard T. ToBI - Team of Bielefeld: The Human-Robot Interaction System for RoboCup@Home 2009. Graz, Austria; 2009.The ToBI robocup team has been newly founded in Jan 2009 in order to proceed existing long-term research in the development of robot companions for domestic environments towards new challenges in more standardized benchmarking procedures, like RoboCup@Home. The main features of the ToBI system are a flexibile Active Memory-based architecture that enables the fast integration of new processing modules and new system behaviors and the modeling of mixed-initiative strategies for multi-modal dialog. The overall goal is an out-of-the-box robot that is able to successfully interact with na¨
ıve users. In this paper we describe the technical basis on which the ToBI system is based and give some insights on previous evaluation experiences
Qualitative design and implementation of human-robot spatial interactions
No full textDespite the large number of navigation algorithms available for mobile robots, in many social contexts they often exhibit inopportune motion behaviours in proximity of people, often with very "unnatural" movements due to the execution of segmented trajectories or the sudden activation of safety mechanisms (e.g., for obstacle avoidance). We argue that the reason of the problem is not only the difficulty of modelling human behaviours and generating opportune robot control policies, but also the way human-robot spatial interactions are represented and implemented. In this paper we propose a new methodology based on a qualitative representation of spatial interactions, which is both flexible and compact, adopting the well-defined and coherent formalization of Qualitative Trajectory Calculus (QTC). We show the potential of a QTC-based approach to abstract and design complex robot behaviours, where the desired robot's motion is represented together with its actual performance in one coherent approach, focusing on spatial interactions rather than pure navigation problems. © Springer International Publishing 2013
A Neuro-Symbolic Approach for Enhanced Human Motion Prediction
No full textReasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy
A computational model of human-robot spatial interactions based on a qualitative trajectory calculus
Full text linkIn this paper we propose a probabilistic sequential model of Human-Robot Spatial Interaction (HRSI) using a well-established Qualitative Trajectory Calculus (QTC) to encode HRSI between a human and a mobile robot in a meaningful, tractable, and systematic manner. Our key contribution is to utilise QTC as a state descriptor and model HRSI as a probabilistic sequence of such states. Apart from the sole direction of movements of human and robot modelled by QTC, attributes of HRSI like proxemics and velocity profiles play vital roles for the modelling and generation of HRSI behaviour. In this paper, we particularly present how the concept of proxemics can be embedded in QTC to facilitate richer models. To facilitate reasoning on HRSI with qualitative representations, we show how we can combine the representational power of QTC with the concept of proxemics in a concise framework, enriching our probabilistic representation by implicitly modelling distances. We show the appropriateness of our sequential model of QTC by encoding different HRSI behaviours observed in two spatial interaction experiments. We classify these encounters, creating a comparative measurement, showing the representational capabilities of the model
Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation from Human Demonstrations
No full textIn this letter, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with. The learning framework allows for both real-time failure detection and regression using Gaussian processes. Our empirical results on two different failure scenarios indicate that given 40 failure state observations, the true positive rate of the failure detection model exceeds 90%, ending with successful recovery actions in more than 90% of all detected cases
Enhancing Human Cooperation with Multimodal Augmented Reality
Full text linkMertes C, Dierker A, Hermann T, Hanheide M, Sagerer G. Enhancing Human Cooperation with Multimodal Augmented Reality. In: Proceedings of the 13th International Conference on Human-Computer Interaction. Lecture Notes in Computer Science, 5610-56. Heidelberg, Germany: Springer; 2009: 447-451.
Humans naturally use an impressive variety of ways to communicate. In this work, we investigate the possibilities of complementing these natural communication channels with artificial ones. For this, augmented reality is used as a technique to add synthetic visual and auditory stimuli to people's perception. A system for the mutual display of the gaze direction of two interactants is presented and its acceptance is shown through a study. Finally, future possibilities of promoting this novel concept of artificial communication channels are explored
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