  We consider the setting of an agent with a fixed body interacting with an
unknown and uncertain external world. We show that models trained to predict
proprioceptive information about the agent's body come to represent objects in
the external world. In spite of being trained with only internally available
signals, these dynamic body models come to represent external objects through
the necessity of predicting their effects on the agent's own body. That is, the
model learns holistic persistent representations of objects in the world, even
though the only training signals are body signals. Our dynamics model is able
to successfully predict distributions over 132 sensor readings over 100 steps
into the future and we demonstrate that even when the body is no longer in
contact with an object, the latent variables of the dynamics model continue to
represent its shape. We show that active data collection by maximizing the
entropy of predictions about the body---touch sensors, proprioception and
vestibular information---leads to learning of dynamic models that show superior
performance when used for control. We also collect data from a real robotic
hand and show that the same models can be used to answer questions about
properties of objects in the real world. Videos with qualitative results of our
models are available at https://goo.gl/mZuqAV.
