The Door and Drawer Reset Mechanisms: Automated Mechanisms for Testing and Data Collection

Robotic manipulation in human environments is a challenging problem for researchers and industry alike. In particular, opening doors/drawers can be challenging for robots, as the size, shape, actuation and required force is variable. Because of this, it can be difficult to collect large real-world datasets and to benchmark different control algorithms on the same hardware. In this paper we present two automated testbeds, the Door Reset Mechanism (DORM) and Drawer Reset Mechanism (DWRM), for the purpose of real world testing and data collection. These devices are low-cost, are sensorized, operate with customized variable resistance, and come with open source software. Additionally, we provide a dataset of over 600 grasps using the DORM and DWRM. We use this dataset to highlight how much variability can exist even with the same trial on the same hardware. This data can also serve as a source for real-world noise in simulation environments.

在人类环境中的机器人操作对研究人员和工业界来说都是一个具有挑战性的问题。特别是，开门/抽屉对机器人来说可能具有挑战性，因为尺寸、形状、驱动方式和所需的力是可变的。因此，可能很难收集大量的真实世界数据集，也很难在相同的硬件上对不同的控制算法进行基准测试。在本文中，我们介绍了两种自动测试平台，门复位机构（DORM）和抽屉复位机构（DWRM），用于真实世界的测试和数据收集。这些设备成本低，配备传感器，以定制的可变电阻运行，并配有开源软件。此外，我们提供了一个使用DORM和DWRM的超过600次抓取的数据集。我们利用这个数据集来强调即使在相同的硬件上进行相同的试验也可能存在很大的可变性。这些数据也可以作为模拟环境中真实世界噪声的一个来源。