Humans interact with deformable objects on a daily basis but this still represents a challenge for robots. To enable manipulation of and interaction with deformable objects, robots need to be able to extract and learn the deformability of objects both prior to and during the interaction. Physics-based models are commonly used to predict the physical properties of deformable objects and simulate their deformation accurately. The most popular simulation techniques are force-based models that need force measurements. In this paper, we explore the applicability of a geometry-based simulation method called meshless shape matching (MSM) for estimating the deformability of objects. The main advantages of MSM are its controllability and computational efficiency that make it popular in computer graphics to simulate complex interactions of multiple objects at the same time. Additionally, a useful feature of the MSM that differentiates it from other physics-based simulation is to be independent of force measurements that may not be available to a robotic framework lacking force/torque sensors. In this work, we design a method to estimate deformability based on certain properties, such as volume conservation. Using the finite element method (FEM) we create the ground truth deformability for various settings to evaluate our method. The experimental evaluation shows that our approach is able to accurately identify the deformability of test objects, supporting the value of MSM for robotic applications.

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.

Robots operating in household environments need to interact with food containers of different types. Whether a container is filled with milk, juice, yogurt or coffee may affect the way robots grasp and manipulate the container. In this paper, we concentrate on the problem of identifying what kind of content is in a container based on tactile and/or visual feedback in combination with grasping. In particular, we investigate the benefits of using unimodal (visual or tactile) or bimodal (visual-tactile) sensory data for this purpose. We direct our study toward cardboard containers with liquid or solid content or being empty. The motivation for using grasping rather than shaking is that we want to investigate the content prior to applying manipulation actions to a container. Our results show that we achieve comparable classification rates with unimodal data and that the visual and tactile data are complimentary.