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The grasping experiment is conducted to verify the adaptation and stability of CTSA Hand. Figure 12 shows CTSA Hand grab a variety of objects with different shapes and sizes well.

Fig. 12 Prototype of soft gripper using electro-conductive yarn Fig. 13 Grasping state estimation using electro-conductive yarn for a cylindrical object Fig. 14 Grasping state estimation using electro-conductive yarn without any object Fig. 15 A food sample (bread) for grasping experiment Fig. 16 Grasping state estimation using electro-conductive yarn for a bread sample.

Fig. 14 Grasping experiment with force sensor.

Fig. 19 Grasping experiment with gravity and without own weight Fig. 20 Flow rate at the time of curve.

Grasping experiments with humans show that the proposed approach is robust enough to correctly capture the intention in demonstrated grasps across a wide range of hand functionality.

During encoding, participants had to pantomime an action to grasp (Experiments 1A & 1B) or to use the objects (Experiment 2) that was either congruent or incongruent with the objects to be retained.

Using a database that consists of 28 objects, we conducted 1200 grasping experiments in a specifically developed simulation environment.

In order to reduce the dimensionality of the grasp postures, we extracted three synergies from data on human grasping experiments and trained a neural network with the features of the objects and the coefficients of the synergies.

However, only the full congruency between the smell of Bombolone and the observation of grasping "Bombolone" (Experiment 3, Fig. 5) produced a clear-cut and specific facilitatory modulation of FDI MEPs amplitude, associated with no significant changes of ADM MEPs.

To further support this connectivity, we conducted a GRASP experiment by differentially expressing two halves of GFP in the pre- and post-synaptic neurons.

For GRASP experiments, we used a mouse monoclonal antibody that specifically recognizes reconstituted GFP (Sigma-Aldrich,  St . Louis  MO, 1 200).