A Rigorous Bayesian Approach to Simultaneous Model Selection and State Estimation for Sensor-based robot tasks

This paper describes a rigorous Bayesian approach to model selection and state estimation for sensorbased robot tasks. The approach is illustrated with an example from autonomous compliant motion: simultaneous Contact Formation recognition and estimation of geometrical parameters. Previous research in this area tries to solve one of the two subproblems, or treats the Contact Formation recognition problem separately, avoiding interaction between the Contact Formation recognition and the geometrical parameter estimation problems. This limits the execution of the task to execution under small uncertainties. This research allows the robot to handle more uncertainty during the execution of its sensor-based task through the estimation of a hybrid joint density of both unknown model and state variables.