Download or read book Learning to Understand Spatial Language for Robotic Navigation and Mobile Manipulation written by Thomas Fleming Kollar and published by . This book was released on 2011 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: (cont.) These statistics are learned from a large database of tagged images such as Flickr, and build off of the model developed in the first component of the thesis. Second, a spatial reasoning component judges how well spatial relations such as "past the computers" describe the path of the robot relative to a landmark. Third, a verb understanding component judges how well spatial verb phrases such as "follow". "meet", "avoid" and "turn right" describe how an agent moves on its own or in relation to another agent. Once trained, our model requires only a metric map of the environment together with the locations of detected objects in order to follow directions through it. This map can be given a priori or created on the fly as the robot explores the environment. In the final chapter of the thesis, we focus on understanding mobile manipulation commands such as, "Put the tire pallet oii the truck." The first contribution of this chapter is the Generalized Grounding Graph (G3 ), which connects language onto grounded aspects of the environment. In this chapter, we relax the assumption that the language has fixed and flat structure and provide a method for constructing a hierarchical probabilistic graphical model that connects each element in a natural language command to an object. place., path or event in the environment. The structure of the G3 model is dynamically instantiated according to the compositional and hierarchical structure of the command, enabling efficient learning and inference. The second contribution of this chapter is to formulate the problem as a discriminative learning problem that maps from language directly onto a robot plan. This probabilistic model is represented as a conditional random field (CRF) that learns the correspondence of robot plans and the language and is able to learn the meanings of complex verbs such as "put" and "take," as well as spatial relations such as "on" and "to."