Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment

Abstract

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/

Figures (12)

• Figure 1: Tac2Real Framework.Tac2Real contains a visuotactile simulator, which can be seamlessly integrated to existing physics engines in a highly parallel way. Tac2real supports online large-scale RL training. Through the systematic approach introduced in TacAlign framework, the sim-to-real gap will be significantly reduced, and Tac2real guarantees a reliable real-world policy deployment.
• Figure 2: Tactile feedback under different contact modes. (a) stationary state; (b) press-down state; (c) move forward state; (d) move backward state.
• Figure 3: Tactile Simulation Framework. Our tactile simulation is an external interface outside the physics engine. Receiving relative quantities from physics environments, Tac2Real starts tactile simulation in a multi-node multi-GPU paralle way with each GPU assigned multi environments' tactile tasks.
• Figure 4: TacAlign Framework. TacAlign narrows the sim-to-real gap from both structured and stochastic perspectives.
• Figure 5: Comparison of different simulation methods. (a) Comparison among Tac2Real, Tacchi, TacSL and real reference in cube indentation test; (b) Comparison of Tac2Real and Tacchi in large rotation deformation; (c) Parallel performance comparison.