Recognizing Texture and Hardness by Touch
Author
Editor
- Raja Chatila
- Alonzo Kelly
- Jean-Pierre Merlet
Summary, in English
We have experimented with different neural network based architectures for bio-inspired self-organizing texture and hardness perception systems. To this end we have developed a microphone based texture sensor and a hardness
sensor that measures the compression of the material at a constant pressure.We have implemented and successfully tested both monomodal systems for texture and hardness perception and multimodal systems that merge texture and hardness data into one representation. All systems were trained and tested
with multiple samples gained from the exploration of a set of 4soft and 4 hard objects of different materials. The monomodal texture system was good at mapping individual objects in a sensible way, the hardness systems was good at mapping individual objects and in addition dividing the objects into categories of hard and soft objects. The multimodal system was successful in merging the two modalities into a representation that performed at least as good as the best recognizer of individual objects, i.e. the texture system, and at the same time
categorizing the objects into hard and soft.
sensor that measures the compression of the material at a constant pressure.We have implemented and successfully tested both monomodal systems for texture and hardness perception and multimodal systems that merge texture and hardness data into one representation. All systems were trained and tested
with multiple samples gained from the exploration of a set of 4soft and 4 hard objects of different materials. The monomodal texture system was good at mapping individual objects in a sensible way, the hardness systems was good at mapping individual objects and in addition dividing the objects into categories of hard and soft objects. The multimodal system was successful in merging the two modalities into a representation that performed at least as good as the best recognizer of individual objects, i.e. the texture system, and at the same time
categorizing the objects into hard and soft.
Department/s
Publishing year
2008
Language
English
Pages
482-487
Publication/Series
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : Acropolis convention center, Nice, France. September 22-26, 2008. Proceedings
Document type
Conference paper
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Computer Vision and Robotics (Autonomous Systems)
Conference name
International Conference on Intelligent Robots and Systems (IROS) 2008
Conference date
2008-09-22 - 2008-09-26
Conference place
Nice, France
Status
Published
Project
- Ikaros: An infrastructure for system level modelling of the brain
Research group
- Lund University Cognitive Science (LUCS)
ISBN/ISSN/Other
- ISBN: 9781424420575
- ISBN: 9781424420582