From sensors to human spatial concepts
(geometric approaches and appearance-based approaches)
Organizers: Zoran Zivkovic, Ben Kröse, Raja Chatila, Henrik Christensen,
Roland Siegwart
Webpage: http://www.science.uva.nl/
The aim of the workshop is to bring together researchers that work on space representations appropriate for communicating with humans and on developing algorithms for relating the robot sensor data to the human spatial concepts. Furthermore, the workshop should renew the discussion about the appearance based and geometric approaches for space modeling in the light of the problem of relating the robot sensor data to the human spatial concepts. Additionally in order to facilitate the discussion a dataset consisting of omnidirectional camera images, laser range readings and robot odometry is provided.
The often used geometric space representations present a natural choice for robot localization and navigation. However, it is hard to communicate with a robot in terms of, for example, 2D (x,y) positions. Some common human spatial concepts are: “the living room”, “the corridor between the living room and the kitchen”; or more general as “a room”, “a corridor”; or more specific and related to objects “behind the TV in the living room”; etc, etc. Appropriate space representation is needed for the natural communication with the robot. Furthermore, the robot should be able to relate its sensor reading to the human spatial concepts.
Suggested topics include, but are not limited to the following areas:
- space representations appropriate for communicating with humans
- papers applying and analyzing the results from cognitive science about the human spatial concepts
- space representations suited to cover cognitive requirements of learning, knowledge acquisition and contextual control
- methods for relating the robot sensor data to the human spatial concepts
- comparison and/or combination of the appearance based and geometric approach for the task of relating the robot sensor data to the human spatial concepts
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