Configuration-space data-driven haptic rendering for multi-link multi-contact haptic interaction

2014 ◽  
Author(s):  
Sangyul Park ◽  
Dongjun Lee
Keyword(s):  
Configuration Space ◽  
Haptic Rendering ◽  
Data Driven ◽  
Haptic Interaction ◽  
Space Data

scholarly journals Inverse design of glass structure with deep graph neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qi Wang ◽  
Longfei Zhang
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Configuration Space ◽  
Glass Structure ◽  
Stable State ◽  
Specific Property ◽  
Inverse Design ◽  
Data Driven ◽  
Common Belief ◽  
Graph Neural Networks

AbstractDirectly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as a sampler, effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as a decider to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

User-based evaluation of data-driven haptic rendering

2010 ◽  
Vol 8 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Raphael Höver ◽  
Massimiliano Di Luca ◽  
Matthias Harders
Keyword(s):  
Haptic Rendering ◽  
Data Driven ◽  
Evaluation Of Data

scholarly journals Recent advances in the application of data science and machine/deep learning methods to research in chemical and biological sciences : a review

2020 ◽  
Author(s):  
Ben Geoffrey
Keyword(s):  
Biological Sciences ◽  
Deep Learning ◽  
Computational Chemistry ◽  
Data Science ◽  
De Novo ◽  
Computational Cost ◽  
Data Driven ◽  
Learning Approaches ◽  
Learning Methods ◽  
Space Data

The rise in application of methods of data science and machine/deep learning in chemical and biological sciences must be discussed in the light of the fore-running disciplines of bio/chem-informatics and computational chemistry and biology which helped in the accumulation ofenormous research data because of which successful application of data-driven approaches have been made possible now. Many of the tasks and goals of Ab initio methods in computational chemistry such as determination of optimized structure and other molecular properties of atoms, molecules, and compounds are being carried out with much lesser computational cost with data-driven machine/deep learning-based predictions. One observes a similar trend in computational biology, wherein, data-driven machine/deep learning methods are being proposed to predict the structure and dynamical of interactions of biological macromolecules such as proteins and DNA over computational expensive molecular dynamics based methods. In the cheminformatics space,one sees the rise of deep neural network-based methods that have scaled traditional structure-property/structure-activity to handle big data to design new materials with desired property and drugs with required activity in deep learning-based de novo molecular design methods. In thebioinformatics space, data-driven machine/deep learning approaches to genomic and proteomic data have led to interesting applications in fields such as precision medicine, prognosis prediction, and more. Thus the success story of the application of data science, machine/deep learning, andartificial intelligence to the disciple of chem/bio-informatics, and computational chemistry and biology has been told in light of how these fore-running disciplines had created huge repositories of data for data-driven approaches to be successful in these disciplines.

Accurate and Efficient CPU/GPU-Based 3-DOF Haptic Rendering of Complex Static Virtual Environments

2009 ◽  
Vol 18 (5) ◽  
pp. 340-360 ◽  
Author(s):  
Jong-Phil Kim ◽  
Beom-Chan Lee ◽  
Hyungon Kim ◽  
Jaeha Kim ◽  
Jeha Ryu
Keyword(s):  
Virtual Environments ◽  
Large Scale ◽  
Search Algorithm ◽  
Search Time ◽  
Haptic Rendering ◽  
Nearest Neighbor Search ◽  
Local Geometry ◽  
Complex Data ◽  
Haptic Interaction ◽  
Depth Maps

This paper proposes a novel, accurate, and efficient hybrid CPU/GPU-based 3-DOF haptic rendering algorithm for highly complex and large-scale virtual environments (VEs) that may simultaneously contain different types of object data representations. In a slower rendering process on the GPU, local geometry near the haptic interaction point (HIP) is obtained in the form of six directional depth maps from virtual cameras adaptively located around the object to be touched. In a faster rendering process on the CPU, collision detection and response computations are performed using the directional depth maps without the need for any complex data hierarchy of virtual objects, or data conversion of multiple data formats. To efficiently find an ideal HIP (IHIP), the proposed algorithm uses a new “abstract” local occupancy map instance (LOMI) and the nearest neighbor search algorithm, which does not require physical memory for storing voxel types during online voxelization and reduces the search time by a factor of about 10. Finally, in order to achieve accurate haptic interaction, sub-voxelization of a voxel in LOMI is proposed. The effectiveness of the proposed algorithm is subsequently demonstrated with several benchmark examples.

Haptic Rendering of Juggling with Encountered Type Interfaces

2011 ◽  
Vol 20 (5) ◽  
pp. 480-501 ◽  
Author(s):  
Emanuele Ruffaldi
Keyword(s):  
Haptic Rendering ◽  
Physical Contact ◽  
Virtual Object ◽  
Haptic Interfaces ◽  
Haptic Interaction ◽  
Multiple Objects ◽  
Multiple Devices ◽  
The Impact ◽  
Psychophysical Test ◽  
Key Parameter

Haptic interaction in a virtual world can be tool mediated or direct; and, among direct interactions, the encountered haptic interfaces provide physical contact only when there is contact with a virtual object. This paper deals with the haptic rendering of the catching and throwing of objects by means of this type of interface. A general model for the rendering of the impact is discussed with the associated formalism for managing multiple objects and multiple devices. Next, a key parameter for simulating the impact is selected by means of a psychophysical test. Finally, a working system is presented with the application of the rendering strategy to the case of haptic juggling, showing the possibility of effectively performing basic juggling patterns with two balls.

Generalized penetration depth for penalty-based six-degree-of-freedom haptic rendering

Robotica ◽  
2008 ◽  
Vol 26 (4) ◽  
pp. 513-524 ◽  
Author(s):  
Maxim Kolesnikov ◽  
Miloš Žefran
Keyword(s):  
Penetration Depth ◽  
Configuration Space ◽  
Depth Estimation ◽  
Haptic Rendering ◽  
Degree Of Freedom ◽  
Object Rotation ◽  
Six Degree Of Freedom ◽  
Closest Point Projection ◽  
Point Projection

SUMMARYExisting penalty-based haptic rendering approaches are based on the penetration depth estimation in strictly translational sense and cannot properly take object rotation into account. We propose a new six-degree-of-freedom (6-DOF) haptic rendering algorithm which is based on determining the closest-point projection of the inadmissible configuration onto the set of admissible configurations. Energy is used to define a metric on the configuration space. Once the projection is found the 6-DOF wrench can be computed from the generalized penetration depth. The space is locally represented with exponential coordinates to make the algorithm more efficient. Examples compare the proposed algorithm with the existing approaches and show its advantages.

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