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The development of human-like skills in robots benefits from understanding how humans perform tasks. Leveraging tactile or haptic training information to develop robot controller training is still an uncultivated land according to our current knowledge. Furthermore, to benefit from the advantages of state-of-the-art learning models for fine-grained manipulation of thumb and forefinger, we must learn from the tactile interactions in addition to the kinematics. We propose a wearable soft sleeve for a human index finger and thumb which includes fluidic pressure sensors that provide tactile information across these two fingers from several 'taxels' or sensory receptors. By combining this with a means of capturing the location and form of contact, through the transfer of ink, we can create unique data sets which provide pressure and contact information. We captured the tactile interactions performed by humans to perform fine task execution such as turning pages. This was analyzed and interpreted to gain insights into human manipulation, and can also serve as a source of training data to guide a compliant robot hand to perform the same task. We consider this experiment as an effort to discover a solution for fine manipulation tasks based on limited tactile data while retaining dexterity and a way to demonstrate and realise fine-grained human-like skills.
Sirithunge et al. (Sun,) studied this question.