轻量高效触觉仿真框架，让机器人学会用触感抓取物体
ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation
- 用数据驱动模型模拟软体接触变形，兼顾精度与速度
- 单卡4090支持4096环境并行训练，吞吐达869 FPS
- 可训练盲抓策略，成功率超84%，适合触觉控制研究
触觉传感器正越来越多地集成到灵巧机械臂中以增强接触感知。然而，依赖触觉信号的学习操纵策略仍面临挑战，主要源于软体仿真在保真度与计算成本之间的权衡。为此，我们提出ETac，一种兼具高保真与高效性的触觉仿真框架，通过轻量级数据驱动的形变传播模型捕捉软体接触动力学，实现高质量仿真并显著提升效率，支持大规模策略训练。作为仿真后端，ETac生成的表面形变估计接近有限元方法（FEM）水平，并可准确建模真实触觉传感器。我们进一步展示其在训练依赖大面积触觉反馈的盲抓策略中的能力。在单张RTX 4090 GPU上，ETac支持4,096个并行环境，总吞吐量达869 FPS。所训练策略在四种物体类型上平均成功率达84.45%，证明了ETac在触觉驱动技能学习中的高效性与可扩展性。
原文摘要 · Abstract (English)
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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