Intelligence without representation
Artificial Intelligence
Learning to Perceive and Act by Trial and Error
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Function-based generic recognition for multiple object categories
CVGIP: Image Understanding
Recognition by functional parts
Computer Vision and Image Understanding - Special issue of funtion-based vision
Interactive recognition and representation of functionality
Computer Vision and Image Understanding - Special issue of funtion-based vision
Adaptive Behavior - Special issue on biologically inspired models of navigation
Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
An Behavior-based Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning Prospective Pick and Place Behavior
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Reinforcement learning of predictive features in affordance perception
Proceedings of the 2006 international conference on Towards affordance-based robot control
Visual learning of affordance based cues
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Extending semantic similarity measurement with thematic roles
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
Case studies of applying Gibson's ecological approach to mobile robots
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A hybrid deliberative layer for robotic agents: fusing DL reasoning with HTN planning in autonomous robots
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In this position paper, we present an outline of the MACS approach to affordance-inspired robot control. An affordance, a concept from Ecological Psychology, denotes a specific relationship between an animal and its environment. Perceiving an affordance means perceiving an interaction possibility that is specific for the animal's perception and action capabilities. Perceiving an affordance does not include appearance-based object recognition, but rather feature-based perception of object functions. The central hypothesis of MACS is that an affordance-inspired control architecture enables a robot to perceive more interaction possibilities than a traditional architecture that relies on appearance-based object recognition alone. We describe how the concept of affordances can be exploited for controlling a mobile robot with manipulation capabilities. Particularly, we will describe how affordance support can be built into robot perception, how learning mechanisms can generate affordance-like relations, how this affordance-related information is represented, and how it can be used by a planner for realizing goal-directed robot behavior. We present both the MACS demonstrator and simulator, and summarize development and experiments that have been performed so far. By interfacing perception and goal-directed action in terms of affordances, we will provide a new way for reasoning and learning to connect with reactive robot control. We will show the potential of this new methodology by going beyond navigation-like tasks towards goal-directed autonomous manipulation in our project demonstrators.