Efficient Tactile Simulation with Differentiability for Robotic Manipulation
Efficient Tactile Simulation with Differentiability for Robotic Manipulation
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发表时间：
2022
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通讯作者：
Jie Xu;Sangwoon Kim;Tao Chen;Alberto Rodriguez;Pulkit Agrawal;W. Matusik;S. Sueda
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作者：
Jie Xu;Sangwoon Kim;Tao Chen;Alberto Rodriguez;Pulkit Agrawal;W. Matusik;S. Sueda
高效模拟触觉传感器能够为在模拟中学习基于触觉的操作策略，并将所学策略迁移到实际系统中开辟新的机遇，但针对密集触觉法向力和剪切力场的快速且可靠的模拟器仍未得到充分探索。我们提出了一种新颖的方法，能够以任意触觉传感器空间布局高效模拟覆盖整个接触面的法向和剪切触觉力场。我们的模拟器还提供触觉力的解析梯度，以加速策略学习。我们进行了大量模拟实验来展示我们的方法，并证明了对于使用基于高分辨率视觉的GelSlim触觉传感器的高精度销钉插入任务，能够成功实现零样本模拟到实际的迁移。相关视频和代码可在以下网址获取：http://tactilesim.csail.mit.edu。
: Efficient simulation of tactile sensors can unlock new opportunities for learning tactile-based manipulation policies in simulation and then transferring the learned policy to real systems, but fast and reliable simulators for dense tactile normal and shear force fields are still under-explored. We present a novel approach for efficiently simulating both the normal and shear tactile force field covering the entire contact surface with an arbitrary tactile sensor spatial layout. Our simulator also provides analytical gradients of the tactile forces to acceler-ate policy learning. We conduct extensive simulation experiments to showcase our approach and demonstrate successful zero-shot sim-to-real transfer for a high-precision peg-insertion task with high-resolution vision-based GelSlim tactile sensors. The videos and code are available at: http://tactilesim.csail.mit.edu.