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Avoiding obstacles in the perceived world has been the classical approach toautonomous mobile robot navigation. However, this usually leads to unnaturaland inefficient motions that significantly differ from the way humans move intight and dynamic spaces, as we do not refrain interacting with the environmentaround us when necessary. Inspired by this observation, we propose a frameworkfor autonomous robot navigation among movable obstacles (NAMO) that is based onthe theory of affordances and contact-implicit motion planning. We consider arealistic scenario in which a mobile service robot negotiates unknown obstaclesin the environment while navigating to a goal state. An affordance extractionprocedure is performed for novel obstacles to detect their movability, and acontact-implicit trajectory optimization method is used to enable the robot tointeract with movable obstacles to improve the task performance or to completean otherwise infeasible task. We demonstrate the performance of the proposedframework by hardware experiments with Toyota's Human Support Robot.

Affordance-Based Mobile Robot Navigation Among Movable Obstacles