  Dexterous in-hand manipulation is a unique and valuable human skill requiring
sophisticated sensorimotor interaction with the environment while respecting
stability constraints. Satisfying these constraints with generated motions is
essential for a robotic platform to achieve reliable in-hand manipulation
skills. Explicitly modelling these constraints can be challenging, but they can
be implicitly modelled and learned through experience or human demonstrations.
We propose a learning and control approach based on dictionaries of motion
primitives generated from human demonstrations. To achieve this, we defined an
optimization process that combines motion primitives to generate robot
fingertip trajectories for moving an object from an initial to a desired final
pose. Based on our experiments, our approach allows a robotic hand to handle
objects like humans, adhering to stability constraints without requiring
explicit formalization. In other words, the proposed motion primitive
dictionaries learn and implicitly embed the constraints crucial to the in-hand
manipulation task.
