Large steps in cloth simulation
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Robust treatment of collisions, contact and friction for cloth animation
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Toward humanoid manipulation in human-centred environments
Robotics and Autonomous Systems
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
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Unstructured, human environments present great challenges and opportunities for robotic manipulation and grasping. Robots that reliably grasp household objects with unknown or uncertain properties would be especially useful, since these robots could better generalize their capabilities across the wide variety of objects found within domestic environments. Within this paper, we address the problem of picking up an object sitting on a plane in isolation, as can occur when someone drops an object on the floor - a common problem for motor-impaired individuals. We assume that the robot has the ability to coarsely position itself in front of the object, but otherwise grasps the object with an open-loop strategy that does not vary from object to object. We present a novel end effector that is capable of robustly picking up a diverse array of everyday handheld objects given these conditions. This straight-forward, inexpensive, nonprehensile end effector combines a compliant finger with a thin planar component with a leading wedge that slides underneath the object. We empirically validated the efficacy of this design through a set of 1096 trials over which we systematically varied the object location, object type, object configuration, and floor characteristics. Our implementation, which we mounted on a iRobot Create, had a success rate of 94.71% on 680 trials, which used 4 floor types with 34 objects of particular relevance to assistive applications in 5 different poses each (4×34×5=680). The robot also had strong performance with objects that would be difficult to grasp using a traditional end effector, such as a dollar bill, a pill, a cloth, a credit card, a coin, keys, and a watch. Prior to this test, we performed 416 trials in order to assess the performance of the end effector with respect to variations in object position.