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UR Vision and Robotics Research

UR vision and robotics research Research in robotics and vision at UR spans a wide range of topics and applications, however one unifying aspect of the various projects is the focus on techniques requiring no or little calibration and thus are suitable to use in natural everyday environments. For example Randal Nelson and Raj Rao are each working on different variations of appearance based recognition which does not need any prior CAD models of the objects. Olac Fuentes has devised and implemented a way of grasping and manipulation a wide variety of objects with a Utah/MIT hand without the use of any object models. Martin Jägersand has developed a method which can learn Visual-Motor manipulation models on-line during the manipulations, instead of requiring a-priori calibration, and shown how to use the method to solve several everyday manipulation problems. Chris Brown and several students are working on vision for a mobile robot. Another benefit is that uncalibrated manipulation techniques lend themselves to better man-machine interfaces, where the robot is instructed for instance by the user pointing at objects (while wearing a "cyberglove" or with the mouse in a picture), or by drawing a "virtual sketch" in a "Mac draw" like program of the desired manipulation. This focus on uncalibrated methods is in contrast to most robot vision systems currently in use, where a carefully engineered environment, and an accurate a-priori model are required, and the manipulation task is described in how many millimeter the robot needs to move in a fixed world coordinate frame.

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