Abstract-We consider the problem of autonomously estimating position and orientation of an object from tactile data. When initial uncertainty is high, estimation of all six parameters precisely is computationally expensive. We propose an efficient Bayesian approach that is able to estimate all six parameters in both unimodal and multimodal scenarios. The approach is termed Scaling Series sampling as it estimates the solution region by samples. It performs the search using a series of successive refinements, gradually scaling the precision from low to high. Our approach can be applied to a wide range of manipulation tasks. We demonstrate its portability on two applications: (1) manipulating a box and (2) grasping a door handle.

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This paper presents a new approach for estimating physical properties of deformable models from experimental measurements. In contrast to most previous work, we introduce a new method based on particle filters which identifies the different stiffness properties for spring-based models. This approach addresses some important limitations encountered with gradient descent techniques which often converge towards ill solutions or remain fixed in local minima conditions.

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