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50 Human Inspired Dexterity in Robotic Manipulation

3.4 CONCLUSIONS

This chapter reviewed our current understanding of how humans learn dex-
terous manipulation based on experimental evidence and computational
models. We are also starting to characterize the space within which the ner-
vous system can, or cannot, generalize learned manipulations. Nevertheless,
more work is needed to fully understand the “rules” that constrain or opti-
mize such generalization, as well as underlying neural mechanisms. Attain-
ing these goals can have a significant impact on neurorehabilitation of
sensorimotor functions of the hand, as well as on robotics research and
design of artificial manipulators.

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