  Exploring and modeling heterogeneous elastic surfaces requires multiple
interactions with the environment and a complex selection of physical material
parameters. The most common approaches model deformable properties from sets of
offline observations using computationally expensive force-based simulators. In
this work we present an online probabilistic framework for autonomous
estimation of a deformability distribution map of heterogeneous elastic
surfaces from few physical interactions. The method takes advantage of Gaussian
Processes for constructing a model of the environment geometry surrounding a
robot. A fast Position-based Dynamics simulator uses focused environmental
observations in order to model the elastic behavior of portions of the
environment. Gaussian Process Regression maps the local deformability on the
whole environment in order to generate a deformability distribution map. We
show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm
and an Optoforce sensor on different deformable surfaces.
