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.

翻译：在人类环境中进行机器人操作对研究人员和工业界而言均是一项挑战性难题。其中，开关门/抽屉对机器人尤为困难，因为其尺寸、形状、驱动方式及所需作用力均存在变量。正因如此，在相同硬件上收集大规模真实世界数据集并基准测试不同控制算法变得极为困难。本文提出两种自动化测试平台——门重置机制（DORM）与抽屉重置机制（DWRM），旨在实现真实世界测试与数据采集。这些装置成本低廉、配备传感器、支持定制化可变阻力操作，并提供开源软件。此外，我们基于DORM与DWRM采集了超过600次抓取的数据集，以此揭示即便在相同硬件上进行相同实验，仍存在显著变异性。这些数据亦可作为仿真环境中真实噪声的来源。