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.
翻译:检测和本地化的接触对于机器人操纵者在非结构化环境中执行接触丰富的任务至关重要。 虽然机器人皮肤可以将机器人臂表面的接触定位为本地化,但这些传感器尚不健全或容易获取。 因此,先前的工程已经探索了使用自我感知观测(如联合速度和托盘)进行本地化。 许多过去的方法都假定机器人在接触事件期间是静态的, 一次就进行单一的接触, 或者有机会获得准确的动态模型和联合电动感测。 在这项工作中, 我们放松这些假设, 并提议使用 Domain 随机化来训练神经网络, 以便在没有联合焦量观测的情况下将机器人臂表面的接触定位化。 我们的方法是使用机器人臂表面的新的圆柱形投影编码, 使网络能够使用脉动层处理输入特性, 并转换电动层来预测接触。 受过训练的网络在一次接触探测中达到91.5%的准确度, 和3.0厘米的平均接触误差。 我们进一步展示了在模拟和现实世界中评估的障碍绘图任务中使用接触本地化模型的应用。