Haptic recognition using hierarchical extreme learning machine with local-receptive-field

In order to perform useful tasks in people’s daily life, robots must be able to both communicate and understand the sensations they experience and may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose an effective hierarchical extreme learning machine with local-receptive-field architecture, while introducing the local receptive field concept in neuroscience and maintaining ELM’s advantages of training efficiency. In this paper, we further extend the LRF-based ELM method to a hierarchical model for haptic classification. Experimental validation on the Penn Haptic Adjective Corpus 2 dataset illustrates that the proposed hierarchical method achieves better recognition performance.

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