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Stochastic Optimal Control (SOC) typically considers noise only in theprocess model, i.e. unknown disturbances. However, in many robotic applicationsinvolving interaction with the environment, such as locomotion andmanipulation, uncertainty also comes from lack of precise knowledge of theworld, which is not an actual disturbance. We analyze the effects of alsoconsidering noise in the measurement model, by developing a SOC algorithm basedon risk-sensitive control, that includes the dynamics of an observer in such away that the control law explicitly depends on the current measurementuncertainty. In simulation results on a simple 2D manipulator, we have observedthat measurement uncertainty leads to low impedance behaviors, a result incontrast with the effects of process noise that creates stiff behaviors. Thissuggests that taking into account measurement uncertainty could be apotentially very interesting way to approach problems involving uncertaincontact interactions.

On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions