Clarification dialogues in human-augmented mapping

Kruijff, Geert-Jan M.; Zender, Hendrik; Jensfelt, Patric; Christensen, Henrik I.

doi:10.1145/1121241.1121290

Cited by 48 publications

(47 citation statements)

References 16 publications

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“…This significantly reduces the number of false positives. We additionally used the method presented in [46,24] for clarification dialogue to handle the few false doors that are still found. The disadvantage with this door detection scheme is that the robot is unable to detect doors where it has not traveled so far.…”

Section: Recognizing Transitions Between Areasmentioning

confidence: 99%

Conceptual spatial representations for indoor mobile robots

Zender

Mozos

Jensfelt

et al. 2008

Robotics and Autonomous Systems

254

182

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We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system.

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Section: Recognizing Transitions Between Areasmentioning

confidence: 99%

Conceptual spatial representations for indoor mobile robots

Zender

Mozos

Jensfelt

et al. 2008

Robotics and Autonomous Systems

254

182

View full text Add to dashboard Cite

show abstract

“…Following the approach by Kruijff et al [2006], the robot can also initiate a clarification dialogue if it detects an inconsistency in its spatial representation, illustrating the mixed-initiative capabilities of the dialogue system. Figure 8.4 sketches the connections between the different modalities implemented in the CoSy explorer.…”

Section: Interactive Map Acquisitionmentioning

confidence: 99%

“…The user denies this fact and the map is updated accordingly. A more detailed explanation of the complete process of clarification dialogues for a similar situation is presented by Kruijff et al [2006]. …”

Section: Episode 2: Clarification Dialoguesmentioning

confidence: 99%

Semantic Labeling of Places with Mobile Robots

Mozos

2010

Springer Tracts in Advanced Robotics

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Zusammenfassung SummaryIndoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment, and to improve its capabilites. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a more intuitive way. Other tasks, like exploration or localization, can also be carried out by the robot in a better way when semantic information is taken into account.In this thesis, we present a method that enables a mobile robot to classify the different places of indoor environments into semantic classes, and then use this information to extend its representations of the environments. The main idea is to classify the position of the robot based on the current observations taken by the robot. In this work, we use as main observations the scans obtained from a laser range sensor. Each scan is represented by a set of features that encode the geometrical properties of the current position. These features are then used to classify the scan into the corresponding semantic class. The output of the classification is represented by a probability distribution over the set of possible semantic classes. This probabilistic representation permits us to apply further probabilistic techniques to improve the final classification, reducing the number of errors. We also present an extension which enables the robot to include other types of observations in the classification, like camera images.This work additionally introduces several applications of the previous approach in different robotic tasks. First, we will show how the semantic information can be used to extract topological maps from indoor environments. In a second application, we present a method that incorporates transitions between different places when classifying a trajectory taken by a mobile robot. It will also be shown that the semantic information can reduce the time needed by the robot in exploration and localization tasks. Finally, we present the semantic classification of places as part of an integrated robotic system designed for interacting with humans using natural language.

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“…The dialog manager will also need to enable a robot to express its intentions and its understanding of the current situation, including responding with alternative approaches to those proposed by the human collaborators or alerting the human team members when a proposed plan is not feasible. This type of clarification (Krujiff, Zender et al 2006) will require the robotic system to understand the speech, interpret the speech in terms of its surroundings and goals, and express itself through speech. An internal model of the communication process will need to be developed.…”

Section: The Audio Channelmentioning

confidence: 99%