This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers.

This paper presents an approach to combine automatic semantic place labeling of robot-generated maps with reasoning on human route descriptions. Enabling robots to understand human route descriptions can simplify HRI situations in household or industrial settings. However, solving this problem requires handling the ambiguity present in route descriptions and the possible unreliability of the semantic perception capabilities of the robot. We address this problem by absorbing these uncertainties in a probability distribution measuring the likelihood of the different interpretations (paths) of a given route description and selecting its MAP solution. The approach is evaluated on a dataset of route descriptions transcribed into a suitable representation using standard information retrieval metrics. These performance measurements indicate that the method can correctly interpret route descriptions even in challenging environments.

It is the aim of our research to explore how multimodal feedback can help a robot to carry out itinerary requests effectively and satisfactory for a human interaction partner. We conducted two studies to evaluate the feedback setup of the Interactive Urban Robot (IURO), which navigates through public space autonomously and finds its way by asking pedestrians for directions. In a Wizard-of-Oz (WOz) experiment with novice users, different feedback modalities and various combinations of them were tested against each other to ascertain the ideal setup of the robot. Subsequently, a cognitive walkthrough with HRI experts was performed to validate the results from the experiment. The results from both studies show that for itinerary requests verbal feedback is most prominent but other feedback modalities may support the conversation by providing reassurance or positive emotions.

Segmentation of speech signals is a crucial task in many types of speech analysis. We present a novel approach at segmentation on a syllable level, using a Bidirectional Long-Short-Term Memory Neural Network. It performs estimation of syllable nucleus positions based on regression of perceptually motivated input features to a smooth target function. Peak selection is performed to attain valid nuclei positions. Performance of the model is evaluated on the levels of both syllables and the vowel segments making up the syllable nuclei. The general applicability of the approach is illustrated by good results for two common databases-Switchboard and TIMIT-for both read and spontaneous speech, and a favourable comparison with other published results.

This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with.

As communication quality in public spaces often is impaired by noisy environment, it is difficult for a robot to retrieve missing task-information from humans. In this paper, different dialog strategies are modeled and evaluated with respect to user experience and error handling capabilities in HRI in order to cope with erroneous speech recognition. Since correct recognition of spoken language is a bottleneck for real-world dialog systems, special emphasis is placed on the issue of adapting dialog strategies to the conditions under which the dialog is held to thereby provide for adaptability of the dialog strategy to variable speech recognition performance. Experimental evaluations are conducted in a fully automated indoor setting, and in a Wizard-of-Oz outdoor setting. Results indicate that a critical point exists, up to which the use of requests for handling miscommunication improves the user experience of a dialog strategy.