Physical Reservoir Computing for Interactive Estimation of Weight from Food Texture in 3D-Printed Soft Matter in Picking Operations

This paper aims to improve the “Gel Biter,” a device that can simultaneously acquire chewing texture data from three different parts (the upper jaw, tongue, and lower jaw) composed of oral mimic end effectors with different softness (Young’s modulus), created using 3D printer technology, for use as a picking device. The Gel Biter utilizes physical reservoir computing, exploiting the deformation of the soft material in the oral model during chewing, to classify the texture of food materials with high accuracy. To apply this principle as a picking system, we examine whether it is possible to determine whether fried foods and fish roe have been cooked correctly and the extent of the weight being gripped based on texture. The results of the gripping object estimation experiments demonstrated that it is possible to distinguish between overcooked fried chicken and properly cooked fried chicken with $\mathbf{9 4 . 7 \%}$ accuracy, and to identify the weight difference of artificial salmon roe in $5 \mathrm{~g}, 10 \mathrm{~g}$, and 20 g increments with $\mathbf{9 4 . 5 \%}$ accuracy. These results suggest that the Gel Biter’s piezoelectric sensors through a physical reservoir computing system can determine information on weight and cooking status based on texture information.