  This paper discuss the integration of risk into a robot control framework for
decommissioning applications in the nuclear industry. Our overall objective is
to allow the robot to evaluate a risk associated with several methods of
completing the same task by combining a set of action sequences. If the
environment is known and in the absence of sensing errors each set of actions
would successfully complete the task. In this paper, instead of attempting to
model the errors associated with each sensing system in order to compute an
exact solution, a set of solutions are obtained along with a prescribed risk
index. The risk associated with each set of actions can then be compared to
possible payoffs or rewards, for instance task completion time or power
consumption. This information is then sent to a high level decision planner,
for instance a human teleoperator, who can then make a more informed decision
regarding the robots actions. In order to illustrate the concept, we introduce
three specific risk measures, namely, the collision risk and the risk of
toppling and failure risk associated with grasping an object. We demonstrate
the results from this foundational study of risk-aware compositional robot
autonomy in simulation using NASA's Valkyrie humanoid robot, and the grasping
simulator HAPTIX.
