Locally optimal navigation among movable obstacles in unknown environments

Mobile manipulators and humanoid robots should be able to utilize their manipulation capabilities to move obstacles out of their way. This concept is captured within the domain of Navigation Among Movable Obstacles (NAMO). While a variety of NAMO algorithms exists, they typically assume full world knowledge. In contrast, real robot systems only have limited sensor range and partial environment knowledge. In this work we present the first NAMO system for unknown environments capable of handling a large set of possible object motions and arbitrary object shapes while guaranteeing optimal decision making for the given knowledge. We demonstrate empirical results with up to 70 obstacles.