Learning Elastic Constitutive Material and Damping Models

Wang, Bin; Deng, Yuanmin; Kry, Paul G.; Ascher, Uri M.; Huang, Hui; Chen, Baoquan

doi:10.1111/cgf.14128

Cited by 7 publications

(8 citation statements)

References 41 publications

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“…Marker‐less methods exist and often use a physics‐based probabilistic approach to track the deformation of the soft object. This is quite robust to noise and occlusion [YL16, WWY*15, WDK*20].…”

Section: Previous Workmentioning

confidence: 99%

“…Our work is less‐dependent on such conditions as we only need depth information that can be captured by an inexpensive consumer‐level LIDAR camera. Other works have also used depth sensors such as Kinect or Intel RealSense [WWY*15, GHZ*20, WDK*20] to capture depth data. In these works, a point‐correspondence between the point cloud and a virtual model is created.…”

Section: Previous Workmentioning

confidence: 99%

“…Selecting correct parameter values in material models is important in a wide range of areas such as modelling soft tissue [BT07], cloth [BTH*03, WOR11, MBT*12, MTB*13, YLL17, SNW20], fabrication [BKS*12, STC*13, CLSM15, CLMK17, YLYW18, WWY*15, WDK*20], haptics [VCO20, PKLD20] and robotics [YL16, BCC17, BBPC19, BSB*20, HBBC19, SLK*20]. Our work is inspired by the problem of producing a computer model of a soft robot that visually matches reality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

Arnavaz

Nielsen

Kry

et al. 2022

Computer Graphics Forum

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In this work, we present a novel approach for calibrating material model parameters for soft body simulations using real data. We use a fully differentiable pipeline, combining a differentiable soft body simulator and differentiable depth rendering, which permits fast gradient-based optimizations. Our method requires no data pre-processing, and minimal experimental set-up, as we directly minimize the L2-norm between raw LIDAR scans and rendered simulation states. In essence, we provide the first markerfree approach for calibrating a soft-body simulator to match observed real-world deformations. Our approach is inexpensive as it solely requires a consumer-level LIDAR sensor compared to acquiring a professional marker-based motion capture system. We investigate the effects of different material parameterizations and evaluate convergence for parameter optimization in both single and multi-material scenarios of varying complexity. Finally, we show that our set-up can be extended to optimize for dynamic behaviour as well.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

Arnavaz

Nielsen

Kry

et al. 2022

Computer Graphics Forum

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“…The high computational power of modern neural networks has opened the door to accurate and efficient datadriven deformable models. The application of neural networks to accelerate deformable simulation includes approaches such as learning of constitutive material models [Wang et al 2019], learning nonlinear corrections to linear deformations [Luo et al 2018], and the design of nonlinear subspace deformation models using autoencoders [Fulton et al 2019].…”

Section: Related Workmentioning

confidence: 99%

Tactile rendering based on skin stress optimization

2020

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We present a method to render virtual touch, such that the stimulus produced by a tactile device on a user's skin matches the stimulus computed in a virtual environment simulation. To achieve this, we solve the inverse mapping from skin stimulus to device configuration thanks to a novel optimization algorithm. Within this algorithm, we use a device-skin simulation model to estimate rendered stimuli, we account for trajectory-dependent effects efficiently by decoupling the computation of the friction state from the optimization of device configuration, and we accelerate computations using a neural-network approximation of the device-skin model. Altogether, we enable real-time tactile rendering of rich interactions including smooth rolling, but also contact with edges, or frictional stick-slip motion. We validate our algorithm both qualitatively through user experiments, and quantitatively on a BioTac biomimetic finger sensor.

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“…A common approach to designing complex elastic material behaviors is to define elastic energy or parameters of stress‐strain functions using weighted scalar basis functions [BBO*09,WOR11, MMO16, SNW20, WDK*20]. However, as we demonstrate in this paper, this approach suffers various problems.…”

Section: Introductionmentioning

confidence: 99%

High‐Order Elasticity Interpolants for Microstructure Simulation

Chan‐Lock

Pérez

Otaduy

2022

Computer Graphics Forum

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We propose a novel formulation of elastic materials based on high‐order interpolants, which fits accurately complex elastic behaviors, but remains conservative. The proposed high‐order interpolants can be regarded as a high‐dimensional extension of radial basis functions, and they allow the interpolation of derivatives of elastic energy, in particular stress and stiffness. Given the proposed parameterization of elasticity models, we devise an algorithm to find optimal model parameters based on training data. We have tested our methodology for the homogenization of 2D microstructures, and we show that it succeeds to match complex behaviors with high accuracy.

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Learning Elastic Constitutive Material and Damping Models

Cited by 7 publications

References 41 publications

Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

Tactile rendering based on skin stress optimization

High‐Order Elasticity Interpolants for Microstructure Simulation

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