A comparative study of contact models for contact-aware state estimation

We study the contact-aware state estimation (CASE) problem, i.e., the problem of estimating the state of an object while it is being actively manipulated by a robot. Several researchers have developed particle filters for this problem. They estimate the state (pose and velocity) of manipulated objects, some physical properties (such as mass and shape), and contact information (such as, gain or loss of contact and transitions between sliding and sticking). However, the effects of various contact and noise models, which can have a huge impact on the estimation results, are obfuscated by implementation details. In this paper, we study the CASE problem arising from a simple pushing task with the goal of shedding light on the fundamental contact modeling choices. Specifically, we evaluate four particle filters based upon four probabilistic state transition models generated from a deterministic multibody dynamics models with rigid or compliant contacts, each of which is augmented by one of two different noise models. Comparisons of these state transition models are carried out through the analysis of real and simulated experiments, the results of which, provide guidance to filter designers.