Automatic grasp planning using shape primitives

Miller, Andrew T.; Knoop, Steffen; Christensen, Henrik I.; Allen, Peter K.

doi:10.1109/robot.2003.1241860

Cited by 565 publications

(388 citation statements)

References 13 publications

(1 reference statement)

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“…In [27,28,29], objects are represented as basic shape primitives and then associated with predefined grasp primitives. In [30], a support vector machine (SVM) is used to learn the grasp quality manifold for a specific hand and simple object shapes.…”

Section: Related Workmentioning

confidence: 99%

Dexterous grasping under shape uncertainty

Hang

Kragić

et al. 2016

Robotics and Autonomous Systems

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An important challenge in robotics is to achieve robust performance in object grasping and manipulation, dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in object shape is parameterized and incorporated as a constraint into grasp planning. The proposed approach is used to plan feasible hand configurations for realizing planned contacts using different robotic hands. A compliant finger closing scheme is devised by exploiting both the object shape uncertainty and tactile sensing at fingertips. Experimental evaluation demonstrates that our method improves the performance of dexterous grasping under shape uncertainty.

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Section: Related Workmentioning

confidence: 99%

Dexterous grasping under shape uncertainty

Hang

Kragić

et al. 2016

Robotics and Autonomous Systems

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“…Some works do this by matching hand pre-grasp postures to prototypical geometric shapes (Miller et al, 2003;Eppner and Brock, 2013). Others learn the mapping of hand-object shape match from real data (Lenz et al, 2013).…”

Section: Interactions Between Hand and Objectmentioning

confidence: 99%

Exploitation of Environmental Constraints in Human and Robotic Grasping

Deimel¹,

Eppner²,

Álvarez-Ruiz³

et al. 2016

Springer Tracts in Advanced Robotics

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We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints.

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“…Instead of directly searching the high dimensional configuration space of robotic hands, this space can be reduced by generating a set of grasp starting positions, hand preshapes [15] or eigengrasps [2] that can then be tested on the object model. Such approaches reduce the dimensionality of the hand configuration space, but doing so implies a corresponding reduction in the accessible hand postures.…”

Section: Related Workmentioning

confidence: 99%

“…Its quality measurement module computes the grasp quality according to all the contacts between the hand and the object, in the form described by Ferrari and Canny [7]. A grasp planning module for primitive shapes, i.e cylinder, sphere, cuboid and cone, is available, allowing users to easily generate grasps [15]. To sample grasps for objects with complex shapes, we alter the module and generate grasps as follows.…”

Section: A Grasp Generation Given the Hand Kinematicsmentioning

confidence: 99%

Learning a real time grasping strategy

Huang

El-Khoury

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning that enables robots to plan new grasps rapidly according to the object's position and orientation. This is achieved by taking a three-step approach. In the first step, we compute a variety of stable grasps for a given object. In the second step, we propose a strategy that learns a probability distribution of grasps based on the computed grasps. In the third step, we use the model to quickly generate grasps. We have tested the statistical method on the 9 degrees of freedom hand of the iCub humanoid robot and the 4 degrees of freedom Barrett hand. The average computation time for generating one grasp is less than 10 milliseconds. The experiments were run in Matlab on a machine with 2.8GHz processor.

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Automatic grasp planning using shape primitives

Cited by 565 publications

References 13 publications

Dexterous grasping under shape uncertainty

Dexterous grasping under shape uncertainty

Exploitation of Environmental Constraints in Human and Robotic Grasping

Learning a real time grasping strategy

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