  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.
