Texture Roughness Estimation Using Dynamic Tactile Sensing

Roughness estimation can help with improving tactile prehension and distinguishing slippage events during object manipulation with a robotic hand. Humans are able to estimate roughness from a small contact area with an object, and adapt manipulation strategies using this information [1]. In order to do the same with a robotic hand fitted with tactile sensors, this article focuses on how to estimate roughness with data from a tactile sensor. We propose a learning algorithm that estimates roughness on a scale from 1 to 5, which was inspired by human tactile capabilities. For more adapted parameters values, this algorithm is optimized with a genetic algorithm. To initialize the scale, we asked 30 people to classify 25 textures on a roughness scale from 1 to 5. The results were used to feed the learning algorithm. After testing our algorithm on those 25 textures, we conclude that even if there are small errors on certain textures, our algorithm is able to adapt itself to new textures and provide a roughness estimation that approximates the human one.