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

TL;DR

This paper explores how incorporating measurement uncertainty into stochastic optimal control affects robotic contact interactions, revealing that it leads to more compliant behaviors in contrast to process noise.

Contribution

It introduces a risk-sensitive control algorithm that explicitly models measurement uncertainty, enhancing control strategies for uncertain contact interactions.

Findings

Measurement uncertainty causes robots to behave with low impedance.

Including measurement noise results in more compliant contact behaviors.

Contrasts with process noise effects, which increase stiffness.

Abstract

Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications involving interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of precise knowledge of the world, which is not an actual disturbance. We analyze the effects of also considering noise in the measurement model, by developing a SOC algorithm based on risk-sensitive control, that includes the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. In simulation results on a simple 2D manipulator, we have observed that measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise that creates stiff behaviors. This suggests that taking into account measurement uncertainty could be a potentially very interesting way to approach problems involving uncertain contact interactions.