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This paper discuss the integration of risk into a robot control framework fordecommissioning applications in the nuclear industry. Our overall objective isto allow the robot to evaluate a risk associated with several methods ofcompleting the same task by combining a set of action sequences. If theenvironment is known and in the absence of sensing errors each set of actionswould successfully complete the task. In this paper, instead of attempting tomodel the errors associated with each sensing system in order to compute anexact solution, a set of solutions are obtained along with a prescribed riskindex. The risk associated with each set of actions can then be compared topossible payoffs or rewards, for instance task completion time or powerconsumption. This information is then sent to a high level decision planner,for instance a human teleoperator, who can then make a more informed decisionregarding the robots actions. In order to illustrate the concept, we introducethree specific risk measures, namely, the collision risk and the risk oftoppling and failure risk associated with grasping an object. We demonstratethe results from this foundational study of risk-aware compositional robotautonomy in simulation using NASA's Valkyrie humanoid robot, and the graspingsimulator HAPTIX.

Integrating Risk in Humanoid Robot Control for Applications in the Nuclear Industry