IEEE Robotics & Automation Magazine - June 2011 - 55

Future Work
Because we use random grasps planned using our available
grasp planners to grasp the objects presented to the robot
and because those grasps tend to be of high quality, approximately 90% of the grasps in the data set succeed in at least
lifting the object. Thus, although the data are useful for differentiating very robust grasps from only marginal grasps,
we would require more data on grasp failures to better elucidate the difference between marginal and bad grasps. In the
future, we plan to obtain data for more random/less good
grasps. We also plan to obtain data for more complex/cluttered scenes than just single objects on a table.
Other planned or possible uses of the data include
l testing object recognition and pose estimation algorithms
l trying to predict when a collision has occurred based on
the recorded accelerometer data from grasps in which
the object hit the table
l testing in-hand object tracking algorithms
l learning graspable features and weights for grasp features from image and point cloud data.
Obtaining grasp recordings by manually placing objects
in the manner used for the Grasp Playpen data set is a
fairly labor-intensive method. Killpack and Kemp have
recently released code and the mechanical design for a PR2
playpen [37] that allows one to record grasps using the
PR2 in a semiautomated manner. Currently, there is no
mechanism for determining the ground-truth pose of the
object being grasped, which is necessary for many of the

Fraction of
Successful Detections

1
0.8
0.6
0.4
0.2
0
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Average Point Distance (cm)

Figure 10. Correct object recognition rates versus the object
detector's match error (average point distance) for our object
recognizer. The blue line shows data from the Grasp Playpen
data set, and the black line shows the Naive Bayes model
chosen to approximate it.

proposed applications of the Grasp Playpen data set. However, automatically generated grasp recordings, if done
with objects with known models, could be annotated using
Mechanical Turk, using a tool that allows a person to
match and pose the correct object model.
Discussion and Conclusions
The data sets discussed in this article are united by the
ROS framework, their collection via the PR2 platform and
their applicability to indoor home and office scenarios.
The data sets' applications, however, force them to differ in
multiple ways.
The Moving People, Moving Platform data set is
intended to be used in an offline knowledge transfer context. In other words, robots are meant to utilize the data in
batch format to train person-detection algorithms, and
then once again in batch format to evaluate these algorithms. This offline mechanism implies that access speed
and data set size are not of primary importance when considering the data set format and contents. This allows the
data to be presented in its raw, loss-less format. Off-line

1.0
Fraction of
Successful Grasps

ground-truth model and pose), to model how often
grasps succeed or fail in real life for different quality values returned by GraspIt!. Histogrammed data from the
Grasp Playpen data set are shown in Figure 11, along with
the piecewise-linear model for grasp quality chosen to
represent it.
We have also used just the recorded object point
clouds to estimate how well other grasp planners and
grasp evaluation algorithms do on real (partial) sensor
data. Because we have the ground-truth model ID and
pose, we can use a geometric simulator such as GraspIt!
to estimate how good an arbitrary grasp is on the true
object geometry. Thus, we can ask a new grasp planner to
generate grasps for a given object point cloud, and then
evaluate in GraspIt! how likely that grasp is to succeed
(with energy values translated into probabilities via the
model described above). Or we can generate grasps using
any grasp planner or at random and ask a new grasp evaluator to say how good it thinks each grasp is (based on
just seeing the point cloud), and again use the groundtruth model pose/geometry to compare those values to
GraspIt!'s success probability estimates. This allows us to
generate data on arbitrarily large numbers of grasps,
rather than just the 490 recorded grasps; we have used
this technique ourselves to evaluate new grasp planners
and evaluators, as well as to create models for them and
perform feature-weight optimization.

0.8
0.6
0.4
Known Points
Model Fit

0.2
0.0

10
20
30
40
50
60
Quality Metric (Lower Is Better)

Figure 11. Experimental grasp success percentages versus
GraspIt!'s grasp quality metric for the PR2 gripper. The blue line
shows binned data from all 490 grasps in the Grasp Playpen
data set, and the black line shows the piecewise-linear model
chosen to approximate it. Blue error bars show 95% confidence
on the mean, computed using bootstrap sampling.

JUNE 2011

IEEE ROBOTICS & AUTOMATION MAGAZINE

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