arXiv 2604.202952026-04-22cs.RO

ETac：一种轻量高效的触觉仿真框架用于学习灵巧操作

ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation

Zhe Xu, Feiyu Zhao, Xiyan Huang, Chenxi Xiao

AI总结：

ETac通过轻量数据驱动模型高效模拟软体接触动态，实现高保真度与效率，支持大规模策略训练，提升触觉感知操作的学习效率和可扩展性。

摘要：

触觉传感器日益集成到灵巧机械臂中以增强接触感知。然而，依赖触觉传感学习操作策略仍具挑战，主要由于软体仿真在保真度与计算成本间的权衡。为此，我们提出ETac，一种触觉仿真框架，通过轻量数据驱动变形传播模型捕捉软体接触动态，实现高仿真质量并提升效率，支持大规模策略训练。当作为仿真后端时，ETac产生的表面变形估计与有限元法相当，并适用于建模真实触觉传感器。随后，我们展示其在训练盲抓取策略中的能力，该策略利用大面积触觉反馈操作多样化物体。在单块RTX 4090 GPU上运行，ETac支持跨4096个并行环境的强化学习，总吞吐量达869 FPS。所获策略在四种物体类型上平均成功率为84.45%，证明ETac在触觉基础技能学习方面具有高效和可扩展的潜力。

英文摘要：

Tactile sensors are increasingly integrated into dexterous robotic manipulators to enhance contact perception. However, learning manipulation policies that rely on tactile sensing remains challenging, primarily due to the trade-off between fidelity and computational cost of soft-body simulations. To address this, we present ETac, a tactile simulation framework that models elastomeric soft-body interactions with both high fidelity and efficiency. ETac employs a lightweight data-driven deformation propagation model to capture soft-body contact dynamics, achieving high simulation quality and boosting efficiency that enables large-scale policy training. When serving as the simulation backend, ETac produces surface deformation estimates comparable to FEM and demonstrates applicability for modeling real tactile sensors. Then, we showcase its capability in training a blind grasping policy that leverages large-area tactile feedback to manipulate diverse objects. Running on a single RTX 4090 GPU, ETac supports reinforcement learning across 4,096 parallel environments, achieving a total throughput of 869 FPS. The resulting policy reaches an average success rate of 84.45% across four object types, underscoring ETac's potential to make tactile-based skill learning both efficient and scalable.

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