Today’s mobile robot perception is insufficient for acting goal-directedly in unconstrained, dynamic everyday environments like a home, a factory, or a city. Subject to restrictions in bandwidth, computer power, and computation time, a robot has to react to a wealth of dynamically changing stimuli in such environments, requiring rapid, selective attention to decisive, action-relevant information of high current utility. Robust and general engineering methods for effectively and efficiently coupling perception, action and reasoning are unavailable. Interesting performance, if any, is currently only achieved by sophisticated robot programming exploiting domain features and specialties, which leaves ordinary users no chance of changing how the robot acts.
The purpose of this volume - outcome of a GI-Dagstuhl Seminar held in Dagstuhl Castle in June 2006 - is to give a first overview on the concept of affordances for the design and implementation of autonomous mobile robots acting goal-directedly in a dynamic environment. The aim is to develop affordance-based control as a method for robotics. The potential of this new methodology will be shown by going beyond navigation-like tasks towards goaldirected autonomous manipulation in the project demonstrators.
Author(s): Verena V. Hafner, Frédéric Kaplan (auth.), Erich Rome, Joachim Hertzberg, Georg Dorffner (eds.)
Series: Lecture Notes in Computer Science 4760 : Theoretical Computer Science and General Issues
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2008
Language: English
Pages: 214
Tags: Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition
Front Matter....Pages -
Interpersonal Maps: How to Map Affordances for Interaction Behaviour....Pages 1-15
Does It Help a Robot Navigate to Call Navigability an Affordance?....Pages 16-26
Learning Causality and Intentional Actions....Pages 27-46
GrAM: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining....Pages 47-62
Affordance-Based Human-Robot Interaction....Pages 63-76
Reinforcement Learning of Predictive Features in Affordance Perception....Pages 77-90
A Functional Model for Affordance-Based Agents....Pages 91-105
Affordances in an Ecology of Physically Embedded Intelligent Systems....Pages 106-121
Use of Affordances in Geospatial Ontologies....Pages 122-139
Learning the Affordances of Tools Using a Behavior-Grounded Approach....Pages 140-158
Function-Based Reasoning for Goal-Oriented Image Segmentation....Pages 159-172
The MACS Project: An Approach to Affordance-Inspired Robot Control....Pages 173-210
Back Matter....Pages -
