Visuotactile sensors are indispensable for contact-rich robotic manipulation tasks. However, policy learning with tactile feedback in simulation, especially for online reinforcement learning (RL), remains a critical challenge, as it demands a delicate balance between physics fidelity and computational efficiency. To address this challenge, we present Tac2Real, a lightweight visuotactile simulation framework designed to enable efficient online RL training. Tac2Real integrates the Preconditioned Nonlinear Conjugate Gradient Incremental Potential Contact (PNCG-IPC) method with a multi-node, multi-GPU high-throughput parallel simulation architecture, which can generate marker displacement fields at interactive rates. Meanwhile, we propose a systematic approach, TacAlign, to narrow both structured and stochastic sources of domain gap, ensuring a reliable zero-shot sim-to-real transfer. We further evaluate Tac2Real on the contact-rich peg insertion task. The zero-shot transfer results achieve a high success rate in the real-world scenario, verifying the effectiveness and robustness of our framework. The project page is: https://ningyurichard.github.io/tac2real-project-page/

翻译：视触觉传感器对于接触密集型机器人操作任务不可或缺。然而，在仿真环境中利用触觉反馈进行策略学习（特别是面向在线强化学习）仍是一项关键挑战，这要求物理保真度与计算效率之间实现精细平衡。为应对这一挑战，我们提出Tac2Real——一个轻量级视触觉仿真框架，旨在支持高效的在线强化学习训练。Tac2Real将预条件非线性共轭梯度增量势接触（PNCG-IPC）方法与多节点、多GPU高吞吐并行仿真架构相结合，能够以交互式速率生成标记点位移场。同时，我们提出TacAlign系统方法，用于缩小结构性与随机性域间隙，确保可靠的零样本仿真到真实迁移。我们进一步在接触密集型插钉任务上评估Tac2Real。零样本迁移结果在真实场景中实现了高成功率，验证了框架的有效性与鲁棒性。项目页面：https://ningyurichard.github.io/tac2real-project-page/