Ergodic Exploration using Binary Sensing for Non-Parametric Shape Estimation

TL;DR

This paper presents a novel approach for shape estimation using ergodic exploration with low-resolution binary sensors, enabling effective object shape reconstruction and object identification without high-resolution sensing.

Contribution

It introduces an ergodic exploration method that leverages binary contact sensors and active information seeking for non-parametric shape estimation, extending to 3D in simulation and real-world experiments.

Findings

Successful shape estimation of various objects

Ability to identify multiple objects in an environment

Effective shape estimation using non-contact motion

Abstract

Current methods to estimate object shape---using either vision or touch---generally depend on high-resolution sensing. Here, we exploit ergodic exploration to demonstrate successful shape estimation when using a low-resolution binary contact sensor. The measurement model is posed as a collision-based tactile measurement, and classification methods are used to discriminate between shape boundary regions in the search space. Posterior likelihood estimates of the measurement model help the system actively seek out regions where the binary sensor is most likely to return informative measurements. Results show successful shape estimation of various objects as well as the ability to identify multiple objects in an environment. Interestingly, it is shown that ergodic exploration utilizes non-contact motion to gather significant information about shape. The algorithm is extended in three dimensions in simulation and we present two dimensional experimental results using the Rethink Baxter robot.