Key Posture Extraction from Object Manipulations Experiments

Nicholas B. Melo; Tetsuyou Watanabe

doi:10.4028/www.scientific.net/AEF.2-3.308

Paper Titles

Research on Dynamic Characteristics of Serial-Parallel Combination Joint with Flexible-Cable
p.285

Structural Optimization of Variable Section Slender Manipulator Based on Sensitivity Analysis Method
p.291

A Relationship between Movement Time and Traveled Distances in Forward/Backward Direction Cooperative Task
p.296

Kinematic Analysis of Three-Wire-Driven Parallel (TWDP) Robot
p.302

Key Posture Extraction from Object Manipulations Experiments
p.308

System Analysis and Design of Gripping Device for Wafer Transfer Robot
p.314

An Overview on Wafer Pre-Aligner
p.320

Multi-Objective Optimal Kinematic Design of 3-TPS/TP Parallel Robot Manipulator
p.324

The Manipulator Cleared the Surface of Fluid Pipelines
p.330

HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 2-3Key Posture Extraction from Object Manipulations...

Key Posture Extraction from Object Manipulations Experiments

Abstract:

In this work we present a novel concept for key posture by looking for grasping similarities among several grasping experiments. To highlight the nature of key posture idea, the object used in experiment had different sizes, although share the same type. Grouping the extracted data by joint, we search for time interval with high data concentration. If this time interval is shared by many joints in the same experiment we can extract key posture from that interval. The key posture can help a robotic hand system to grasp, control and manipulate the object through a specific task.

By email View Pdf

You have full access to the following eBook

Read eBook

Info:

Periodical:

Advanced Engineering Forum (Volumes 2-3)

Pages:

308-313

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.2-3.308

Citation:

Cite this paper

Online since:

December 2011

Authors:

Nicholas B. Melo, Tetsuyou Watanabe

Keywords:

Grasping Experiment, Grasping Patterns, Key Posture, Robotic Hand, Robotic Learning

Export:

RIS, BibTeX

Permissions:

Creative Commons CC BY 4.0

Citation:

References

[1] Diego R. Faria, Ricardo Martins, Jorde Dias; Learning motion patterns from multiple observation along the action phases of manipulative tasks. 2010 IEEE/RSJ international conference on intelligent robots and System.

Google Scholar

[2] Koichi Ogawara, Yasufumi Tanabe, Ryo Kurazume, Tsutomu Hasegawa; Detecting Repeated Motion Patterns via Dynamic Programming using Motion Density; 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, May 12-17, (2009).

DOI: 10.1109/robot.2009.5152643

Google Scholar

[3] Javier Romero, Thomas Feix, Hedvig Kjellstrom, Danica Kragic. Spatio-Temporal modeling of grasping actions. 2010 IEEE/RSJ international conference on intelligente robots and system.

DOI: 10.1109/iros.2010.5650701

Google Scholar

[4] Salvador Cobos, Manuel Ferre, Rafael Aracil; Simplified Human Hand Models based on Grasping Analysis; The 2010 IEEE/RSJ International Conference on Intelligente Robots and System, October 18-22, 2010, Taipei, Taiwan.

DOI: 10.1109/iros.2010.5651479

Google Scholar

[5] Sylvain Calinon, Florent Guenter and Aude Billard; On Learning, Representing and Generalizing a task in a Humanoid Robot; IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS, PART B.

DOI: 10.1109/tsmcb.2006.886952

Google Scholar

[6] Matei Ciocarlie, Corey Goldfeder, Peter Allen ; Dimensionality reduction for hand-independent dexterous robotic grasping.

DOI: 10.1109/iros.2007.4399227

Google Scholar

[7] Andrew T. Miller; GraspIt!: A Versatile Simulator for Robotic Grasping; 2001 COLUMBIA UNIVERSITY.

Google Scholar

[8] Carlo Ferrari, John Canny; Planning Optimal Grasps; Proceeding of the 1992 IEEE, International Conference on Robotics and Automation, Nice, France – (1992).

DOI: 10.1109/robot.1992.219918

Google Scholar