论文标题
接触机器人臂的本地化，而无需转矩感应
Contact Localization for Robot Arms in Motion without Torque Sensing
论文作者
论文摘要
检测和本地化触点对于机器人操纵器在非结构化环境中执行接触量丰富的任务至关重要。尽管机器人皮肤可以将触点定位在机器人臂表面，但这些传感器尚不强大或易于访问。因此，先前的工作已经使用本体感受观测（例如关节速度和扭矩）进行了接触定位探索。过去的许多方法都认为机器人在接触事件中是静态的，一次接触一次，或者可以访问精确的动态模型和关节扭矩感测。在这项工作中，我们放宽了这些假设，并提出使用域随机化来训练神经网络，以将机器人臂的触点进行运动，而无需观察到关节扭矩。我们的方法使用了机器人臂表面的新型圆柱投影编码，该圆柱形投影允许网络使用卷积层处理输入特征和转置卷积层以预测接触。训练有素的网络可实现91.5％的接触检测精度，平均接触定位误差为3.0厘米。我们进一步证明了在模拟和现实世界中评估的障碍物映射任务中接触本地化模型的应用。
Detecting and localizing contacts is essential for robot manipulators to perform contact-rich tasks in unstructured environments. While robot skins can localize contacts on the surface of robot arms, these sensors are not yet robust or easily accessible. As such, prior works have explored using proprioceptive observations, such as joint velocities and torques, to perform contact localization. Many past approaches assume the robot is static during contact incident, a single contact is made at a time, or having access to accurate dynamics models and joint torque sensing. In this work, we relax these assumptions and propose using Domain Randomization to train a neural network to localize contacts of robot arms in motion without joint torque observations. Our method uses a novel cylindrical projection encoding of the robot arm surface, which allows the network to use convolution layers to process input features and transposed convolution layers to predict contacts. The trained network achieves a contact detection accuracy of 91.5% and a mean contact localization error of 3.0cm. We further demonstrate an application of the contact localization model in an obstacle mapping task, evaluated in both simulation and the real world.
