  In this paper, we propose a data-driven skill learning approach to solve
highly dynamic manipulation tasks entirely from offline teleoperated play data.
We use a bilateral teleoperation system to continuously collect a large set of
dexterous and agile manipulation behaviors, which is enabled by providing
direct force feedback to the operator. We jointly learn the state conditional
latent skill distribution and skill decoder network in the form of
goal-conditioned policy and skill conditional state transition dynamics using a
two-stage generative modeling framework. This allows one to perform robust
model-based planning, both online and offline planning methods, in the learned
skill-space to accomplish any given downstream tasks at test time. We provide
both simulated and real-world dual-arm box manipulation experiments showing
that a sequence of force-controlled dynamic manipulation skills can be composed
in real-time to successfully configure the box to the randomly selected target
position and orientation; please refer to the supplementary video,
https://youtu.be/LA5B236ILzM.
