Towards affordance-based robot control : international seminar, Dagstuhl Castle, Germany, June 5-9, 2006 : revised papers
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書誌事項
Towards affordance-based robot control : international seminar, Dagstuhl Castle, Germany, June 5-9, 2006 : revised papers
（Lecture notes in computer science, 4760 . Lecture notes in artificial intelligence）
Springer, c2008
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注記
Includes bibliographical references and author index
"LNCS sublibrary : SL 7 Artifical Intelligence"--T.p. verso
内容説明・目次
内容説明
Today's mobile robot perception is insu?cient for acting goal-directedly in - constrained, 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 - vironments, requiring rapid, selective attention to decisive, action-relevant - formation of high current utility. Robust and general engineering methods for e?ectively and e?ciently coupling perception, action, and reasoning are unava- able. Interesting performance, if any, is currently only achieved by sophisticated robot programming exploiting domain features and specialties, which leaves - dinary users no chance of changing how the robot acts. The latter facts are high barriers for introducing, for example, service robots into human living or work environments. In order to overcome these barriers, additonal R&D e?orts are required. The European Commission is undert- ing a determined e?ort to fund related basic, inter-disciplinary research in a line of Strategic Objectives, including the Cognitive Systems calls in their 6th Framework Programme (FP6, [1]), and continuing in the 7th Framework P- gramme.
OneofthefundedCognitiveSystemsprojectsisMACS("multi-sensory autonomous cognitive systems interacting with dynamic environments for p- ceiving and using a?ordances").
目次
Interpersonal Maps: How to Map Affordances for Interaction Behaviour.- Does It Help a Robot Navigate to Call Navigability an Affordance?.- Learning Causality and Intentional Actions.- GrAM: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining.- Affordance-Based Human-Robot Interaction.- Reinforcement Learning of Predictive Features in Affordance Perception.- A Functional Model for Affordance-Based Agents.- Affordances in an Ecology of Physically Embedded Intelligent Systems.- Use of Affordances in Geospatial Ontologies.- Learning the Affordances of Tools Using a Behavior-Grounded Approach.- Function-Based Reasoning for Goal-Oriented Image Segmentation.- The MACS Project: An Approach to Affordance-Inspired Robot Control.
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