Learning haptic representation for manipulating deformable food objects

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Abstract
Manipulation of complex deformable semi-solids such as food objects is an important skill for personal robots to have. In this work, our goal is to model and learn the physical properties of such objects. We design actions involving use of tools such as forks and knives that obtain haptic data containing information about the physical properties of the object. We then design appropriate features and use supervised learning to map these features to certain physical properties (hardness, plasticity, elasticity, tensile strength, brittleness, adhesiveness). Additionally, we present a method to compactly represent the robot's beliefs about the object's properties using a generative model, which we use to plan appropriate manipulation actions. We extensively evaluate our approach on a dataset including haptic data from 12 categories of food (including categories not seen before by the robot) obtained in 941 experiments. Our robot prepared a salad during 60 sequential robotic experiments where it made a mistake in only 4 instances.

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