Tac2Real: オンライン強化学習とゼロショット実機展開のための信頼性の高いGPU視触覚シミュレーション
Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment
接触を伴う操作タスク向けに、物理精度と計算効率を両立した軽量な視触覚シミュレーションフレームワークTac2Realを提案し、実機へのゼロショット転移を高成功率で実現した。
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著者: Ningyu Yan, Shuai Wang, Xing Shen, Hui Wang, Hanqing Wang, Yang Xiang, Jiangmiao Pang
分類: cs.RO
原文アブストラクト
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/
