Learning active tactile perception through belief-space control

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

Keywords: Active perception, differentiable filtering, manipulation, tactile sensing

TL;DR: Using differentiable filtering methods, we learn how to touch objects to better perceive their properties

Abstract:

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.

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Submission Number: 11