  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.
