To enhance the hybrid estimation system's ability to distinguish the compliance of harder objects, several strategies could be implemented. First, increasing the resolution of the tactile sensors would allow for finer detection of deformation differences in harder materials. Current camera-based tactile sensors, like GelSight, may not capture minute variations in surface displacement for rigid objects, which limits the system's accuracy.
Second, incorporating advanced machine learning techniques, such as transfer learning or domain adaptation, could help the model generalize better across different material types. By training the model on a more diverse dataset that includes a wider range of hard materials, the system could learn to recognize subtle compliance differences more effectively.
Additionally, integrating multi-modal sensing approaches could provide complementary data. For instance, combining tactile sensing with acoustic or ultrasonic sensors could yield insights into material properties that are not solely reliant on surface deformation. This multi-faceted approach would allow the system to leverage different physical phenomena to improve compliance estimation.
Finally, refining the analytical models used in conjunction with the neural network could enhance the overall accuracy. By incorporating more complex geometrical and material models that account for the unique properties of harder materials, the system could provide more reliable estimates of Young's modulus for these objects.