Learning active tactile perception through belief-space control

Jean-François Tremblay, Johanna Hansen, David Meger, Francois Robert Hogan, Gregory Dudek

Abstract:

Robot operating in an open world can encounter
novel objects with unknown physical properties, such as mass,
friction, or size. It is desirable to be able to sense those
property through contact-rich interaction, before performing
downstream tasks with the objects. We propose a method for
autonomously learning active tactile perception policies, by
learning a generative world model leveraging a differentiable
bayesian filtering algorithm, and designing an information-
gathering model predictive controller. We test the method on
three simulated tasks: mass estimation, height estimation and
toppling height estimation. Our method is able to discover
policies which gather information about the desired property
in an intuitive manner.