DFKI-LT - Long Short-Term Memory for Affordances Learning
Long Short-Term Memory for Affordances Learning
2 Proceedings of the 9th International Conference on Epigenetic Robotics,
Lund University Cognitive Studies number 146,
This paper addresses the problem of sensorimotor learning from the perspective of affordances learning of simple objects. We are developing a scenario where a robotic arm interacts with a polyflap, a simple 3-dimensional geometrical object. We perform experiments with a simulated arm using a physics simulator, but we plan to use also a real arm. The robot interacts with the object by pushing it in different ways. We use Recurrent Neural Networks to predict the arm and object poses during this interaction, given a discrete set of random actions that the robot can produce.
Files: BibTeX, RoaKruijffEPIROB09.pdf