%{search_type} search results

The STanford Artificial Intelligence Robot (STAIR) project aims to construct and develop robots that can work in real-world environments such as homes and offices, where they will interact regularly with people. To work in such places, manipulation is an essential ability, and one fundamental skill of manipulation is grasping. Robotic grasping has therefore long been a focus of the STAIR project. Because STAIR needs to operate in dynamic and unknown environments, no model of the world or objects to grasp is available; instead, it must perceive all this from its visual sensors. This makes grasping a very difficult problem because data from visual sensors is noisy and incomplete. A robust grasping system for STAIR using only visual input is therefore presented here. One approach used in the past to solve the grasping problem is to separate it into two components, the first for finding feasible robotic arm/hand grasp configurations, and the second to select the best one of these configurations as the final grasp to execute. This is known as the comparative approach. We build upon and generalize this approach. For the first component, which we denote as “search,” a previously developed image-based classifier that we developed is used to find plausible grasping points, then a search strategy is applied to find feasible grasp configurations. For the second component, which we denote as “selection,” we notice that there are some properties of a candidate grasp configuration that are indicative of good/bad grasps, and that these properties remain consistent across different objects. Moreover, these properties can be easily and reliably computed even if the 3-D point cloud from visual sensors is incomplete. We therefore present a selection algorithm that computes these features for candidate grasp candidates, and using a trained logistic classifier produces a quality score for each grasp. The grasp with maximum score is then executed. Extensive experiments using this grasping system were performed on STAIR. In particular, reasonable performance was found in all three tasks assessed: grasping single objects of various appearances, shapes, and sizes, grasping objects in cluttered environments, and applying the algorithm on a separate robotic platform to unload items from a dishwasher. Performance was comparable in each case, which indicates that the grasping system is effective in its targeted environments and is generalizable to other robots.

This thesis considers the problem of automatically collecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules.In particular, it describes a system by which a SLAM-generated map of an office environment can be annotated with text labels such as room numbers and the names of office occupants. These labels are acquired automatically from signs posted on walls throughout a building using a feed-forward system that collects a set of building images; uses a trained classifier to detect candidate text regions on those images; runs optical character recognition on those regions to extract the textual content; and post-processes the text string set with alignment information before placing each string at its original location on the map. Such a system faces difficulties using current text recognition techniques, as standard approaches to optical character recognition fail when faced with text from natural images as opposed to document text. Despite these difficulties, our system provides a series of additions to the typical mapping pipeline which provide practically useful results. In fact, our text detection and recognition system, combined with other ingredients, allow the robot to generate an annotated map from which it can recognize named locations specified by a user 84% of the time.