Neural network models of haptic shape perception

Summary, in English

Three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. One model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (SOM). The two other models replace the tensor product operation with a novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). The two T-MPSOM models differ in the procedure employed to calculate the neural activation. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. All the models learned to map different shapes to different areas of the SOM, and the tensor product model as well as one of the T-MPSOM models also learned to discriminate individual test objects. (c) 2007 Elsevier B.V. All rights reserved.

• Author: Magnus Johnsson; Christian Balkenius
• Department/s: Cognitive Science
• Publishing year: 2007
• Language: English
• Pages: 720-727
• Publication/Series: Robotics and Autonomous Systems
• Volume: 55
• Issue: 9
• Links: http://dx.doi.org/10.1016/j.robot.2007.05.003
• Document type: Journal article
• Publisher: Elsevier
• Topic: Computer Vision and Robotics (Autonomous Systems)
• Keywords: robotic hand; tensor product; haptic perception; self-organizing map
• Status: Published
• Project: Ikaros: An infrastructure for system level modelling of the brain
• ISBN/ISSN/Other: ISSN: 0921-8890