Maps, Places, and Worlds for Robots

Vision is a powerful sense that permits a robot to look around itself and gather information both about the immediate present and the near future. The future arrives through the more distant physical space through which the robot can move, and the possible actions and events that may arise. To know the future a robot needs to parse the world with the aid of its models and experience. Lasers and other active sensors have proven their ability to provide accurate geometric information. But context and the meaning of the space surrounding the robot, the objects and the actions they permit are only accessible with the more complete sensory input of vision. Vision as a sensor is computationally demanding, but resources have improved to make it practical. Moreover there has been a convergence of the interests of roboticists and vision scientists – both want to explore and act in the world. Many of us have accepted that we must learn the patterns of data using machine learning, but we must also integrate categorical descriptions of our world, prototypical information that no individual robot is yet capable of learning. Vision provides the anchoring for concepts. I will discuss recent advances and trends linking vision and robotics through spatial descriptions and the connections with objects, actions, and meaning.