  We investigate an experiential learning paradigm for acquiring an internal
model of intuitive physics. Our model is evaluated on a real-world robotic
manipulation task that requires displacing objects to target locations by
poking. The robot gathered over 400 hours of experience by executing more than
100K pokes on different objects. We propose a novel approach based on deep
neural networks for modeling the dynamics of robot's interactions directly from
images, by jointly estimating forward and inverse models of dynamics. The
inverse model objective provides supervision to construct informative visual
features, which the forward model can then predict and in turn regularize the
feature space for the inverse model. The interplay between these two objectives
creates useful, accurate models that can then be used for multi-step decision
making. This formulation has the additional benefit that it is possible to
learn forward models in an abstract feature space and thus alleviate the need
of predicting pixels. Our experiments show that this joint modeling approach
outperforms alternative methods.
