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• Tac2Real integrates the Preconditioned Nonlinear Conjugate Gradient Incremental Potential Contact (PNCG-IPC) method with a multi-node, multi-GPU parallel architecture to generate marker displacement fields at interactive rates.method 95%
• The Tac2Real framework enables efficient online reinforcement learning training for visuotactile policies.method 90%
• Tac2Real achieves reliable zero-shot sim-to-real transfer for contact-rich manipulation tasks.result 90%
• The proposed TacAlign methodology systematically reduces both structured and stochastic sources of the sim-to-real domain gap.method 90%
• Tac2Real is designed as a lightweight plugin compatible with mainstream robot simulators like Isaac Lab and MuJoCo.technical 95%
• The PNCG-IPC solver formulates elastodynamic contact simulation as a variational optimization problem to balance physics fidelity and computational efficiency.technical 85%
• Tac2Real's architecture supports multi-node, multi-GPU parallelization for high-throughput simulation.technical 90%
• The framework outputs marker displacement fields directly as the tactile representation to mitigate uncertainties from optical rendering.technical 85%

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