轻量级触觉仿真框架，支持在线强化学习与零样本真实部署
Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment
- 结合快速接触算法与多GPU并行架构，实现实时触觉仿真
- 零样本迁移在插桩任务中成功率高，验证了仿真可靠性
- 适合需要真实世界快速部署的接触密集型机器人任务
视觉触觉传感器对接触密集型机器人操作任务至关重要。然而，在仿真中利用触觉反馈进行策略学习，尤其是在线强化学习（RL），仍面临重大挑战，需在物理保真度与计算效率之间取得平衡。为此，我们提出Tac2Real，一种轻量级视觉触觉仿真框架，支持高效在线RL训练。该框架融合预条件非线性共轭梯度增量势能接触（PNCG-IPC）方法与多节点多GPU高吞吐并行仿真架构，可在交互速率下生成标记位移场。同时，我们提出系统性方法TacAlign，缩小结构化与随机性域差距，确保可靠的零样本仿真到现实迁移。我们在接触密集的插桩任务上评估Tac2Real，零样本迁移在真实场景中达到高成功率，验证了框架的有效性与鲁棒性。
原文摘要 · Abstract (English)
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/
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