Towards Affordance-Based Robot Control International Seminar, Dagstuhl Castle, Germany, June 5-9, 2006, Revised Papers

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.

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.