PneuTac：通过统一MPM-高斯泼溅模拟实现软体气动机器人的触觉操作

中文摘要 AI 辅助

软体机器人和触觉传感器在精细操作任务中展现出巨大潜力。软体气动机器人通过柔顺性实现安全接触，而基于视觉的触觉传感器提供高分辨率的触觉感知。然而，使用柔顺机器人学习触觉操作一直具有挑战性，其瓶颈在于缺乏高效的仿真。现有仿真器通常孤立地建模这些组件，并且存在难以高效克服的校准差距。我们提出了PneuTac，一个用于软体气动机器人触觉反馈操作的综合框架。我们利用物质点法（MPM）对软体机器人和可变形触觉膜的动力学进行建模，并使用3D高斯泼溅（3DGS）进行渲染。真实到仿真建模通过简单的基于视觉的方法完成，然后训练动作和感知网络以使用替代模型进行高效仿真。我们利用该框架驱动一个触觉引导的流程，在仿真中收集演示。通过在定制设计的气动软手指（带有触觉感知尖端）上的实验，以及额外的跨设备评估，我们展示了PneuTac能够准确建模带有触觉传感器的软体机器人，并且使用仿真增强演示训练的策略在三个真实世界的接触丰富柔顺操作任务上优于在相同真实数据上训练的基线，使其成为在柔顺硬件上进行触觉操作的实用框架。

英文摘要

Soft robots and tactile sensors have demonstrated great potential in delicate manipulation tasks. Soft pneumatic robots enable safe contact through compliance, and vision-based tactile sensors offer high-resolution touch perception. However, learning tactile manipulation with compliant robots has been challenging, bottlenecked by the lack of efficient simulation. Existing simulators typically model them in isolation, and exhibit large calibration gaps that are difficult to overcome efficiently. We present PneuTac, a unified framework for tactile-feedback manipulation with soft pneumatic robots. We leverage the material point method (MPM) for modelling the dynamics of the soft robot and the deformable tactile membrane, and 3D Gaussian splatting (3DGS) for rendering. Real-to-sim modelling is done with a simple vision-based method, to then train action and perception networks for efficient simulation with surrogate models. We use the framework to drive a tactile-guided pipeline to collect demonstrations in simulation. Through experiments on a custom-designed pneumatic soft finger with a tactile sensing tip, together with additional cross-device evaluations, we show that PneuTac is capable of accurately modelling soft robots with tactile sensors, and that policies trained with simulation-augmented demonstrations outperform baselines trained on the same real data on three real-world contact-rich compliant manipulation tasks, making it a practical framework for tactile manipulation on compliant hardware.

发表机构

• University of Oxford（牛津大学）

机构由 AI 辅助整理，请以论文原文为准。