  Building hand-object models for dexterous in-hand manipulation remains a
crucial and open problem. Major challenges include the difficulty of obtaining
the geometric and dynamical models of the hand, object, and time-varying
contacts, as well as the inevitable physical and perception uncertainties.
Instead of building accurate models to map between the actuation inputs and the
object motions, this work proposes to enable the hand-object systems to
continuously approximate their local models via a self-identification process
where an underlying manipulation model is estimated through a small number of
exploratory actions and non-parametric learning. With a very small number of
data points, as opposed to most data-driven methods, our system self-identifies
the underlying manipulation models online through exploratory actions and
non-parametric learning. By integrating the self-identified hand-object model
into a model predictive control framework, the proposed system closes the
control loop to provide high accuracy in-hand manipulation. Furthermore, the
proposed self-identification is able to adaptively trigger online updates
through additional exploratory actions, as soon as the self-identified local
models render large discrepancies against the observed manipulation outcomes.
We implemented the proposed approach on a sensorless underactuated Yale Model O
hand with a single external camera to observe the object's motion. With
extensive experiments, we show that the proposed self-identification approach
can enable accurate and robust dexterous manipulation without requiring an
accurate system model nor a large amount of data for offline training.
