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We present a method for classifying the quality of near-contact grasps using spatial metrics that are recoverable from sensor data. Current methods often rely on calculating precise contact points, which are difficult to calculate in real life, or on tactile sensors or image data, which may be unavailable for some applications. Our method, in contrast, uses a mix of spatial metrics that do not depend on the fingers being in contact with the object, such as the object's approximate size and location. The grasp quality can be calculated {\em before} the fingers actually contact the object, enabling near-grasp quality prediction. Using a random forest classifier, the resulting system is able to predict grasp quality with 96\% accuracy using spatial metrics based on the locations of the robot palm, fingers and object. Furthermore, it can maintain an accuracy of 90\% when exposed to 10\% noise across all its inputs. 

Grasping a simple object from the side is easy --- unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans --- when faced with this challenge --- adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either ``puppetteering'' a robotic manipulator that is identical~(geometrically and kinematically) to a popular underactuated robotic manipulator~(the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware.