Book ChapterDOI
Random Forest for Modeling and Rendering of Viscoelastic Deformable Objects
Hojun Cha,Amit Bhardwaj,Chaeyong Park,Seungmoon Choi +3 more
- Vol. 535, pp 48-53
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TLDR
This paper proposes a new data-driven approach based on a well known machine learning technique: random forest, which trains the random forest for regression for estimating the input-output mapping between discrete-time interaction data collected on a homogeneous deformable object.Abstract:
In the recent past, data-driven approaches have gained importance for modeling and rendering of haptic properties of deformable objects. In this paper, we propose a new data-driven approach based on a well known machine learning technique: random forest. We train the random forest for regression for estimating the input-output mapping between discrete-time interaction data (position/displacement and force) collected on a homogeneous deformable object. Unlike currently existing data-driven approaches, we use at most 1% of the recorded interaction data for the training of the random forest. Even then, the trained random forest model reproduces all the interactions used for the training with a good accuracy. This also provides promising results on unseen data. When employed for haptic rendering, the model estimates smooth and stable interaction forces at an update rate more than 650 Hz.read more
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Data-Driven Haptic Rendering—From Viscous Fluids to Visco-Elastic Solids
TL;DR: In this paper, the authors present extensions of their earlier work on data-driven haptic rendering, where the selection of appropriate data dimensions is guided by the structure of the generalized Maxwell model.
Journal ArticleDOI
Sensing, Acquisition, and Interactive Playback of Data-based Models for Elastic Deformable Objects
TL;DR: An automated system to build models of elastic deformable objects and to render these models in an interactive virtual environment to enable haptic model acquisition through active probing with a haptic device.
Journal ArticleDOI
Data-Driven Haptic Modeling and Rendering of Viscoelastic and Frictional Responses of Deformable Objects
TL;DR: An extended data-driven haptic rendering method capable of reproducing force responses during pushing and sliding interaction on a large surface area using a novel input variable set for the training of an interpolation model that incorporates the position of a proxy - an imaginary contact point on the undeformed surface.
Proceedings ArticleDOI
Data-driven haptics: Addressing inhomogeneities and computational formulation
A. Sianov,Matthias Harders +1 more
TL;DR: A new computational formulation for obtaining the radial basis function reconstructions of the reaction force signal is suggested, Inspired by Compressive Sensing, which employs an ℓ1-minimization with a random selection strategy.
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