A neural network architecture for physically-consistent haptic rendering

doi:10.1007/s10055-025-01199-w

A neural network architecture for physically-consistent haptic rendering

2025-07-22

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DOI：10.1007/s10055-025-01199-w

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We develop a method for realistic haptic rendering of generalized solids that employs graph neural networks. In order to give these neural networks the required realism, biases of different types are used. In the case of solids with (hyper)elastic, hence reversible, behaviour, we use Hamiltonian neural networks. These networks employ a cognitive bias that ensures energy conservation during the simulation. In the case of dissipative solids (particularly visco-hyperelastic solids) we extend the cognitive bias so that it takes into account entropy production. In this way, regardless of the type of solid considered, the developed neural networks are able to provide the haptic devices with the necessary information for a realistic and, above all, physically consistent rendering. The results are tested using a set of haptic gloves and virtual reality glasses, which have shown excellent realism, but not without certain limitations due to the state of the art in the development of hardware of this type.

Keywords:

Real-time simulation

Haptic synthesis

Thermodynamics

Scientific machine learning

A neural network architecture for physically-consistent haptic rendering

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