  Robots operating in an open world will encounter novel objects with unknown
physical properties, such as mass, friction, or size. These robots will need to
sense these properties through interaction prior to performing downstream tasks
with the objects. We propose a method that autonomously learns tactile
exploration policies by developing a generative world model that is leveraged
to 1) estimate the object's physical parameters using a differentiable Bayesian
filtering algorithm and 2) develop an exploration policy using an
information-gathering model predictive controller. We evaluate our method on
three simulated tasks where the goal is to estimate a desired object property
(mass, height or toppling height) through physical interaction. We find that
our method is able to discover policies that efficiently gather information
about the desired property in an intuitive manner. Finally, we validate our
method on a real robot system for the height estimation task, where our method
is able to successfully learn and execute an information-gathering policy from
scratch.
