Introduction to AI Robotics
Computer and Robot Vision
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Biological Cybernetics - Special Issue: Dynamic Principles
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
Anticipatory Behavior in Adaptive Learning Systems
The initial development of object knowledge by a learning robot
Robotics and Autonomous Systems
Robotics and Autonomous Systems
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
A relational representation for procedural task knowledge
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Multimodal word learning from infant directed speech
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Traversability: A Case Study for Learning and Perceiving Affordances in Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Visual learning of affordance based cues
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Some Basic Principles of Developmental Robotics
IEEE Transactions on Autonomous Mental Development
Cognitive Developmental Robotics: A Survey
IEEE Transactions on Autonomous Mental Development
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
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In this paper, we show that through self-interaction and self-observation, an anthropomorphic robot equipped with a range camera can learn object affordances and use this knowledge for planning. In the first step of learning, the robot discovers commonalities in its action-effect experiences by discovering effect categories. Once the effect categories are discovered, in the second step, affordance predictors for each behavior are obtained by learning the mapping from the object features to the effect categories. After learning, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. We argue that the learning system proposed shares crucial elements with the development of infants of 7-10 months age, who explore the environment and learn the dynamics of the objects through goal-free exploration. In addition, we discuss goal emulation and planning in relation to older infants with no symbolic inference capability and non-linguistic animals which utilize object affordances to make action plans.