Intelligence in the Now: Robust Intelligence in Complex Domains

Our overall goal is to develop the estimation, planning, and control techniques necessary to enable robots to perform robustly and intelligently in complex uncertain domains. Robots operating in complex, unknown environments have to deal explicitly with uncertainty. Sensing is increasingly reliable, but inescapably local: robots cannot see, immediately, inside cupboards, under collapsed walls, or into nuclear containment vessels. Task planning, whether in household and disaster-relief domains, requires explicit consideration of uncertainty and the selection of actions at both the task and motion levels to support gathering information. Our approach to robust behavior in uncertain domains is founded on the notion of integrating estimation, planning, and execution in a feedback loop. A plan is made, based on the current belief state; the first step is executed; an observation is obtained; the belief state is updated; the plan is recomputed, if necessary, etc. We call this online replanning. Our work in this grant has developed an initial version of such a planner and demonstrated it for controlling the behavior of an autonomous mobile-manipulation robot.