IEEE Robotics & Automation Magazine - June 2011 - 45

All three data sets presented in this article share a com- l the Grasp Playpen data set, containing both propriocepmon framework, both in software [the robot operating systive data from the robot's sensors and ground-truth
tem (ROS)] and in hardware [the personal robot 2 (PR2)].
information from a human operator as the robot perThis allows us to compare and contrast them from multiformed a large number of grasping tasks.
ple points of view, including data collection tools, annotaWhile new data sets can be independently made availtion methodology, and applications.
able of code or application releases, they can provide staUnlike its counterpart from the factory floor, a robot ble interfaces for algorithm development. The intention
operating in an unstructured environment can expect to be is not to tie data sets to specific code instances. Rather,
confronted by the unexpected. Generality is an important both the data set and code can follow rigorous (yet possiquality for robots intended to work in typical human set- bly independent) release cycles, while explicitly tagging
tings. Such a robot must be able to navigate around and compatibility between specific versions (e.g., Algorithm
interact with people, objects, and obstacles in the environ- 1.0 has been trained on
ment, with a level of generality reflecting typical situations Data 3.2). The potential *
of daily living or working. In such cases, an extensive benefits of using such a
Generality is an important
knowledge base, containing and possibly synthesizing release model for data sets
information from multiple relevant scenarios, can be a include the following:
quality for robots intended
valuable resource for robots aiming to cope with the varia- l defining a stable interface to the data set
bility of the human world.
to work in typical human
component of a release
Recent years have seen a growing consensus that one of
will allow external rethe keys to robotic applications in unstructured environments
settings.
searchers to provide
lies in collaboration and reusable functionality [1], [2]. A
their own modified *
result has been the emergence of a number of platforms and
and/or extended verframeworks for sharing operational building blocks, usually
sions of the data to the community, knowing that it will
in the form of code modules, with functionality ranging from
be directly usable by anyone running the algorithmic
low-level hardware drivers to complex algorithms. By using a
component
set of now well-established guidelines, such as stable, documented interfaces and standardized communication proto- l similarly, a common data set and interface can enable a
direct comparison of multiple algorithms [5]
cols, this type of collaboration has accelerated development
toward complex applications. However, a similar set of meth- l a self-contained distribution, combining a compatible
code release and the sensor data needed to test and use
ods for sharing and reusing data has been slower to emerge.
them, can increase research and development commuIn this article, we present three data sets that use the
nity by including groups that do not have access to hardsame robot framework, comprising the ROS [1], [3] and
ware platforms.
PR2 platform [4]. While sharing the underlying software
and hardware architecture, they
address different components of a
mobile manipulation task: interacting
with humans and grasping objects.
They also highlight some of the different choices available for creating and
using data sets for robots. As such,
this comparison endeavors to begin a
dialog on the format of data sets for
robots. The three data sets, exemplified in Figure 1, are as follows:
l the Moving People, Moving Platform data set, containing robot
perception data in office environments with an emphasis on person detection
l the Household Objects and Grasps
(a)
(b)
(c)
data set, containing 3-D models of
objects common in household and
office environments, as well as a Figure 1. Examples from the data sets presented in this study. (a) Section of a camera
large set of grasp points for each image annotated with people's locations and outlines. (b) 3-D models of household
objects with grasp point information (depicted by green arrows) generated in simulation.
model precomputed in a simulated (c) The PR2 robot executing a grasp while recording visual and proprioceptive
information.
environment
JUNE 2011

IEEE ROBOTICS & AUTOMATION MAGAZINE

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2011