Robust Manipulation of Deformable Objects Using Model Based Technique

2000 ◽  
pp. 1-14 ◽  
Author(s):  
T. Wada ◽  
S. Hirai ◽  
H. Mori ◽  
S. Kawamura
Keyword(s):  
Deformable Objects ◽  
Model Based

Robust Manipulation of Deformable Objects Using A Model Based Technique

2000 ◽  
Vol 2000.1 (0) ◽  
pp. 445-446
Author(s):  
Takahiro Wada ◽  
Hiroshige Mori ◽  
Nobuyoshi Koike ◽  
Shinichi Hirai ◽  
Sadao Kawamura
Keyword(s):  
Deformable Objects ◽  
Model Based

scholarly journals Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World

Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 46
Author(s):  
Peng Chang ◽  
Taşkın Padır
Keyword(s):  
Real World ◽  
Geometric Model ◽  
Model Parameters ◽  
Power Cable ◽  
Deformable Objects ◽  
Model Based ◽  
Revolute Joints ◽  
New Strategy ◽  
Flexible Objects ◽  
3D Geometric Model

Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach.

Active shape model-based real-time tracking of deformable objects

2005 ◽  
Author(s):  
Sangjin Kim ◽  
Daehee Kim ◽  
Jeongho Shin ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Active Shape Model ◽  
Deformable Objects ◽  
Shape Model ◽  
Model Based ◽  
Active Shape ◽  
Real Time Tracking

Robust Shape Control of Deformable Objects Using Model-Based Techniques

2011 ◽  
Vol 25 (16) ◽  
pp. 2099-2123 ◽  
Author(s):  
Jadav Das ◽  
Nilanjan Sarkar
Keyword(s):  
Shape Control ◽  
Deformable Objects ◽  
Model Based

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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