  Dexterous multi-fingered hands can provide robots with the ability to
flexibly perform a wide range of manipulation skills. However, many of the more
complex behaviors are also notoriously difficult to control: Performing in-hand
object manipulation, executing finger gaits to move objects, and exhibiting
precise fine motor skills such as writing, all require finely balancing contact
forces, breaking and reestablishing contacts repeatedly, and maintaining
control of unactuated objects. Learning-based techniques provide the appealing
possibility of acquiring these skills directly from data, but current learning
approaches either require large amounts of data and produce task-specific
policies, or they have not yet been shown to scale up to more complex and
realistic tasks requiring fine motor skills. In this work, we demonstrate that
our method of online planning with deep dynamics models (PDDM) addresses both
of these limitations; we show that improvements in learned dynamics models,
together with improvements in online model-predictive control, can indeed
enable efficient and effective learning of flexible contact-rich dexterous
manipulation skills -- and that too, on a 24-DoF anthropomorphic hand in the
real world, using just 4 hours of purely real-world data to learn to
simultaneously coordinate multiple free-floating objects. Videos can be found
at https://sites.google.com/view/pddm/
