Going beyond the perception of affordances: Learning how to actualize them through behavioral parameters
In this paper, we propose a method that enables a robot to learn not only the existence of affordances provided by objects, but also the behavioral parameters required to actualize them, and the prediction of effects generated on the objects in an unsupervised way. In a previous study, it was shown that through self-interaction and self-observation, analogous to an infant, an anthropomorphic robot can learn object affordances in a completely unsupervised way, and use this knowledge to make plans in its perceptual space. This paper extends the affordances model proposed in that study by using parametric behaviors and including the behavior parameters into affordance learning and goal-oriented plan generation. Furthermore, for handling complex behaviors and complex objects (such as execution of precision grasp on a mug), the perceptual processing is improved by using a combination of local and global features. Finally, a hierarchical clustering algorithm is used to discover the affordances in non-homogenous feature space. In short, object affordances for object manipulation are discovered together with behavior parameters based on the monitored effects.
Emre Ugur Erhan Oztop Erol (S)ahin
Biological ICT,National Institute of Information and Communication Technology,Kyoto,Japan Cognitive Cognitive Mechanisms Labs.,Advanced Telecommunications Institute International,Kyoto,Japan Biologica KOVAN Research Lab.,Department of Computer Engineering,Middle East Technical University,Ankara,Turke
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4768-4773
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）