What do contacts tell about an object?

Among all senses the sense of touch is the only one without which humans are not able to manipulate objects. Similarly, tactile sense is invaluable for robotic manipulation in uncertain environments. It is however not thoroughly understood to what extent properties of the robot environment can be inferred from the tactile sense. This paper presents a novel approach that allows to study how much information a robot can optimally learn from a single tactile exploration attempt. Our method makes use of a simulator as an internal memory for the robot. The evaluation is based on assessing how much information error minimization between predicted and actual sensor readings can provide about the environment. This paper focuses on evaluating geometric parameters in a transportation task. Experiments performed with a set of objects with various shapes indicate that a single exploration action is not guaranteed to provide much information for all uncertain factors if the attempt is not originally planned for information gain in mind. Moreover, the information gain for different attributes varies significantly depending on the object geometry.