论文标题
通过触觉传感从模拟传输转移的SIM到实现的稳定性预测
Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing
论文作者
论文摘要
机器人仿真一直是数据驱动的操作任务的重要工具。但是，大多数现有的仿真框架都缺乏与触觉传感器的物理相互作用的高效和准确模型，也没有逼真的触觉模拟。这使得基于触觉的操纵任务的SIM转交付仍然具有挑战性。在这项工作中，我们通过建模接触物理学来整合机器人动力学和基于视觉的触觉传感器的模拟。该接触模型使用机器人最终效应器上的模拟接触力来告知逼真的触觉输出。为了消除使用现实数据的机器人动力学，触点模型和触觉光学模拟器的物理模拟器，我们使用现实世界数据校准了物理模拟器，然后我们证明了系统对零击的SIMS对型SIM卡对局部抓取稳定性预测的有效性，而我们可以在各个对象上达到90.7％的平均准确性。实验揭示了将我们的模拟框架应用于更复杂的操纵任务的潜力。我们在https://github.com/cmurobotouch/taxim/tree/taxim-robot上开放仿真框架。
Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at https://github.com/CMURoboTouch/Taxim/tree/taxim-robot.
