Abstract

Dexterous manipulation enables repositioning of
objects and tools within a robot’s hand. When applying dexterous
manipulation to unknown objects, exact object models
are not available. Instead of relying on models, compliance and
tactile feedback can be exploited to adapt to unknown objects.
However, compliant hands and tactile sensors add complexity
and are themselves difficult to model. Hence, we propose acquiring
in-hand manipulation skills through reinforcement learning,
which does not require analytic dynamics or kinematics models.
In this paper, we show that this approach successfully acquires
a tactile manipulation skill using a passively compliant hand.
Additionally, we show that the learned tactile skill generalizes
to novel objects.