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This paper describes grasp point selection on an item of clothing randomly placed on a table. The input data for our proposed method is a range image captured from a fixed, 3D range camera. Hem elements are extracted from the data, and their relationships are characterized for both similarity measures and grasp point evaluation. Experiments using real images, targeting a piece of clothing, show the...
In this paper we present a method for the generation of hand postural synergies for different precision grasp types to be used in dextrous robot hands. Our method records the robot hand motions while teleoperated by human subjects via a dataglove, doing different grasp types on a series of objects. This exploits the fact that humans automatically compensate for calibration errors on the glove to robot...
The perception of the surrounding world depends on noisy sensors which introduce uncertainty. When we develop algorithms for grasping with robotic hands it is not enough to assume the best estimate of the environment - if there is a measure of uncertainty we need to account for it. This paper presents a control law which augments a grasp controller with the ability to prefer known or unseen regions...
This paper proposes a method for bin-picking for objects without assuming the precise geometrical model of objects. We consider the case where the shape of objects are not uniform but are similarly approximated by cylinders. By using the point cloud of a single object, we extract the probabilistic properties with respect to the difference between an object and a cylinder and consider applying the...
We address the problem of representations for anthropomorphic robot hands and their suitability for use in methods for learning or control. We approach hand configuration from the perspective of ultimate hand function and propose 2 parameterizations based on the ability of the hand to engage oppositional forces. These parameters can be extracted from grasp examples making them suitable for use in...
Multi-fingered robot grasping is a challenging problem that is difficult to tackle using hand-coded programs. In this paper we present an imitation learning approach for learning and generalizing grasping skills based on human demonstrations. To this end, we split the task of synthesizing a grasping motion into three parts: (1) learning efficient grasp representations from human demonstrations, (2)...
The grasping skill is an indispensable quality for general service robotics. In a home-like natural environment, manipulated objects may be unknown in advance, which prevents the use of a combination of traditional grasp planning and visual pose estimation to realize grasping. Stereo vision is an inexpensive and relatively general sensor for 3-D objects. However, the quality of the sensor data from...
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