Shape analysis and hand preshaping for grasping

Bard, C.; Troccaz, Jocelyne; Vercelli, Gianni

doi:10.1109/iros.1991.174427

Cited by 28 publications

(13 citation statements)

References 7 publications

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“…From this point fo view, the dB has the potentiality to support much more research than is described here; in fact, here we have been neglecting the orientation of the hand at the time of grasping, the dynamics embedded in the reaching phase (containing a lot of information more, see, e.g. [34], [35]) and the possibility of exploiting the two points of view (we only use one of the cameras); but these data are available in the dB. Such a research could finally lead to a significant advance also in robotic grasping, too, as the reconstructed grasp might be somehow mapped onto the robotic end-effector in a teloperated setup.…”

Section: Discussionmentioning

confidence: 99%

Using Object Affordances to Improve Object Recognition

Castellini

Tommasi

Noceti

et al. 2011

IEEE Trans. Auton. Mental Dev.

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Abstract-The problem of object recognition has not yet been solved in its general form. The most successful approach to it so far relies on object models obtained by training a statistical method on visual features obtained from camera images. The images must necessarily come from huge visual datasets, in order to circumvent all problems related to changing illumination, point of view, etc.We hereby propose to also consider, in an object model, a simple model of how a human being would grasp that object (its affordance). This knowledge is represented as a function mapping visual features of an object to the kinematic features of a hand while grasping it. The function is practically enforced via regression on a human grasping database.After describing the database (which is publicly available) and the proposed method, we experimentally evaluate it, showing that a standard object classifier working on both sets of features (visual and motor) has a significantly better recognition rate than that of a visual-only classifier.

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Section: Discussionmentioning

confidence: 99%

Using Object Affordances to Improve Object Recognition

Castellini

Tommasi

Noceti

et al. 2011

IEEE Trans. Auton. Mental Dev.

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“…The entire prehensile process effectively occurs before the hand has even touched the object and thus the vision system plays a very important role [Bard et al, 1991, Iberall, 1987. Our system uses the Early Cognitive Vision methods of Pugeault et al [Pugeault, 2008, Hartley andZisserman, 2000], which makes a minimal number of assumptions about the object, and has been successfully implemented to determine good grasp locations [Detry et al, 2009].…”

Section: Appendix Dynamical Systems Motor Primitivesmentioning

confidence: 99%

Grasping with Vision Descriptors and Motor Primitives

Kroemer

Detry

Piater

et al. 2011

Informatics in Control, Automation and Robotics

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Abstract:Grasping is one of the most important abilities needed for future service robots. Given the task of picking up an object from betweem clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which may not always be available. Therefore, methods for executing grasps are required, which perform well with information gathered from only standard stereo vision, and make only a few necessary assumptions about the task environment. We propose techniques that reactively modify the robot's learned motor primitives based on information derived from Early Cognitive Vision descriptors. The proposed techniques employ non-parametric potential fields centered on the Early Cognitive Vision descriptors to allow for curving hand trajectories around objects, and finger motions that adapt to the object's local geometry. The methods were tested on a real robot and found to allow for easier imitation learning of human movements and give a considerable improvement to the robot's performance in grasping tasks.

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“…Robotic grasping and manipulation is a well researched area [8,9,10]. In many cases the results are supported by modelling and/or simulation, concerned with force or form closure, and the interaction between the object and a dexterous end effector.…”

Section: Robotic Modellingmentioning

confidence: 99%

An Approach to the Simulation of Robotic Systems Using XML Based Configuration Files

Sunderland

Crowder

Damper

2004

Volume 4: 24th Computers and Information in Engineering Conference

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This paper presents a environment using the eXtensible Markup Language ,XML, to describe a robotic systems in a format suitable for simulation, and to support the integration of several programming environments to create a flexible physical simulation system. Data exchange via open-standard based plain text files allows the system components to be loosely-coupled, rather than combined them into a single integrated development environment. This ensures that the most appropriate tools can be used for each component and the system can be extended with minimal disruption. Those parts of the system that require real-time data exchange use simple UNIX socket-based interactions, which are configured using shared XML configuration files. The environment is demonstrated by the simulation of a simple task using a SCARA robot.

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Shape analysis and hand preshaping for grasping

Cited by 28 publications

References 7 publications

Using Object Affordances to Improve Object Recognition

Using Object Affordances to Improve Object Recognition

Grasping with Vision Descriptors and Motor Primitives

An Approach to the Simulation of Robotic Systems Using XML Based Configuration Files

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