Deep Learning for Detecting Robotic Grasps
Authors: Ian Lenz, Honglak Lee, Ashutosh Saxena
ICLR 2013

Abstract: In this work, we consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. We present a two-step cascaded structure, where we have two deep networks, with the top detections from the first one re-evaluated by the second one. The first deep network has fewer features, is therefore faster to run and makes more mistakes. The second network has more features and therefore gives better detections. Unlike previous works that need to design these features manually, deep learning gives us flexibility in designing such multi-step cascaded detectors.

Deep Learning for Detecting Robotic Grasps
Ian Lenz,† Honglak Lee,∗ and Ashutosh Saxena †
† Department of Computer Science, Cornell University.
∗ Department of EECS, University of Michigan, Ann Arbor.
Email: ianlenz@cs.cornell.edu, honglak@eecs.umich.edu, asaxena@cs.cornell.edu
Abstract—We consider the problem of detecting robotic grasps
in an RGB-D view of a scene containing objects. In this work,
we apply a deep learning approach to solve this problem, which
avoids time-consuming hand-design of features. This presents two
main challenges. First, we need to evaluate a huge number of
candidate grasps. In order to make detection fast and robust,
we present a two-step cascaded system with two deep networks,
where the top detections from the ﬁrst are re-evaluated by
the second. The ﬁrst network has fewer features, is faster to
run, and can effectively prune out unlikely candidate grasps.
The second, with more features, is slower but has to run
only on the top few detections. Second, we need to handle
multimodal inputs effectively, for which we present a method
that applies structured regularization on the weights based on
multimodal group regularization. We show that our method
improves performance on an RGBD robotic grasping dataset,
and can be used to successfully execute grasps on two different
robotic platforms. 1
Keywords: Robotic Grasping, deep learning, RGB-D multi-
modal data, Baxter, PR2, 3D feature learning.
I. I NTRODUCTION
Robotic grasping is a challenging problem involving percep-
tion, planning, and control. Some recent works [54, 56, 28, 67]
address the perception aspect of this problem by converting it
into a detection problem in which, given a noisy, partial view
of the object from a camera, the goal is to infer the top loca-
tions where a robotic gripper could be placed (see Figure 1).
Unlike generic vision problems based on static images, such
robotic perception problems are often used in closed loop with
controllers, so there are stringent requirements on performance
and computational speed. In the past, hand-designing features
has been the most popular method for several robotic tasks
[40, 32]. However, this is cumbersome and time-consuming,
especially when we must incorporate new input modalities
such as RGB-D cameras.
Recent methods based on deep learning [1] have demon-
strated state-of-the-art performance in a wide variety of tasks,
including visual recognition [35, 60], audio recognition [39,
41], and natural language processing [12]. These techniques
are especially powerful because they are capable of learning
useful features directly from both unlabeled and labeled data,
avoiding the need for hand-engineering.
1Parts of this work were presented at ICLR 2013 as a workshop paper,
and at RSS 2013 as a conference paper. This version includes signiﬁcantly
extended related work, algorithmic descriptions, and extensive robotic exper-
iments which were not present in previous versions.
However, most work in deep learning has been applied in
the context of recognition. Grasping is inherently a detection
problem, and previous applications of deep learning to detec-
tion have typically focused on speciﬁc vision applications such
as face detection [45] and pedestrian detection [57]. Our goal
is not only to infer a viable grasp, but to infer the optimal grasp
for a given object that maximizes the chance of successfully
grasping it, which differs signiﬁcantly from the problem of
object detection. Thus, the ﬁrst major contribution of our work
is to apply deep learning to the problem of robotic grasping, in
a fashion which could generalize to similar detection problems.
The second major contribution of our work is to propose
a new method for handling multimodal data in the context
of feature learning. The use of RGB-D data, as opposed
to simple 2D image data, has been shown to signiﬁcantly
improve grasp detection results [28, 14, 56]. In this work, we
present a multimodal feature learning algorithm which adds a
structured regularization penalty to the objective function to
be optimized during learning. As opposed to previous works
in deep learning, which either ignore modality information at
the ﬁrst layer (i.e., encourage all features to use all modalities)
[59] or train separate ﬁrst-layer features for each modality
[43, 61], our approach allows for a middle-ground in which
each feature is encouraged to use only a subset of the input
modalities, but is not forced to use only particular ones.
We also propose a two-stage cascaded detection system
based on deep learning. Here, we use fewer features for the
ﬁrst pass, providing faster, but only approximately accurate
detections. The second pass uses more features, giving more
accurate detections. In our experiments, we found that the
ﬁrst deep network, with fewer features, was better at avoiding
overﬁtting but less accurate. We feed the top-ranked rectangles
from the ﬁrst layer into the second layer, leading to robust
early rejection of false positives. Unlike manually designed
two-step features as in [28], our method uses deep learning,
which allows us to learn detectors that not only give higher
performance, but are also computationally efﬁcient.
We test our approach on a challenging dataset, where
we show that our algorithm improves both recognition and
detection performance for grasping rectangle data. We also
show that our two-stage approach is not only able to match
the performance of a single-stage system, but, in fact, improves
results while signiﬁcantly reducing the computational time
needed for detection.
In summary, the contributions of this paper are:
• We present a deep learning algorithm for detecting
arXiv:1301.3592v6  [cs.LG]  21 Aug 2014
Fig. 1: Detecting robotic grasps: Left: A cluttered lab scene labeled with rectangles corresponding to robotic grasps for objects in the
scene. Green lines correspond to robotic gripper plates. We use a two-stage system based on deep learning to learn features and perform
detection for robotic grasping. Center: Our Baxter robot “Yogi” successfully executing a grasp detected by our algorithm. Right: The grasp
detected for this case, in the RGB (top) and depth (bottom) images obtained from Kinect.
robotic grasps. To the best of our knowledge, this is the
ﬁrst work to do so.
• In order to handle multimodal inputs, we present a new
way to apply structured regularization to the weights to
these inputs based on multimodal group regularization.
• We present a multi-step cascaded system for detection,
signiﬁcantly reducing its computational cost.
• Our method outperforms the state-of-the-art for rectangle-
based grasp detection, as well as previous deep learning
algorithms.
• We implement our algorithm on both a Baxter and a
PR2 robot, and show success rates of 84% and 89%,
respectively, for executing grasps on a highly varied set
of objects.
The rest of the paper is organized as follows: We discuss
related work in Section II. We present our two-step cascaded
detection system in Section III, and some additional details
in Section IV. We then describe our feature learning algo-
rithm and structured regularization method in Section V. We
present our experiments in Section VI, and discuss results in
Section VII. We then present experiments on both Baxter and
PR2 robots in Section VIII. We present several interesting
directions for future work in Section IX, then conclude in
Section X.
II. R ELATED WORK
A. Robotic Grasping
In this section, we will focus on perception- and learning-
based approaches for robotic grasping. For a more complete
review of the ﬁeld, we refer the reader to review papers by
Bohg et al. [4], Sahbani et al. [53], Bicchi and Kumar [2] and
Shimoga [58].
Most works deﬁne a “grasp” as an end-effector conﬁg-
uration which achieves partial or complete form- or force-
closure of a given object. This is a challenging problem
because it depends on the pose and conﬁguration of the robotic
gripper as well as the shape and physical properties of the
object to be grasped, and typically requires a search over a
large number of possible gripper conﬁgurations. Early works
[34, 44, 49] focused on testing for form- and force-closure,
and synthesizing grasps fulﬁlling these properties according to
some hand-designed “quality score” [17]. More recent works
have reﬁned these deﬁnitions [50]. These works assumed full
knowledge of object shape and physical properties.
Grasping Given 3D Model: Fast synthesis of grasps for
known 3D models remains an active research topic [14, 20,
65], with recent methods using advanced physical simulation
to ﬁnd optimal grasps. Gallegos et al. [18] performed opti-
mization of grasps given both a 3D model of the object to be
grasped and the desired contact points for the robotic gripper.
Pokorny et al. [48] deﬁne spaces of graspable objects, then
map new objects to these spaces to discover grasps. However,
these works are only applicable when the full 3D model of
the object is exactly known, which may not be the case when
a robot is interacting with a new environment. We note that
some of these physics-based approaches might be combined
with our approach in a multi-pass system, discussed further in
Sec. IX.
Sensing for Grasping: In a real-world robotic setting, a robot
will not have full knowledge of the 3D model and pose of an
object to be grasped, but rather only incomplete information
from some set of sensors such as color or depth cameras,
tactile sensors, etc. This makes the problem of grasping
signiﬁcantly more challenging [4], as the algorithm must use
more limited and potentially noisier information to detect a
good grasp. While some works [10, 46] simply attempt to
estimate the poses of known objects and then apply full-model
grasping algorithms based on these results, others avoid this
assumption, functioning on novel objects which the algorithm
has not seen before.
Such works often made use of other simplifying assump-
tions, such as assuming that objects belong to one of a
set of primitive shapes [47, 6], or are planar [42]. Other
works produced impressive results for speciﬁc cases, such as
grasping the corners of towels [40]. While such works escape
the assumption of a fully-known object model, hand-coded
grasping rules have a hard time dealing with the wide range
of objects seen in real-world human environments, and are
difﬁcult and time-consuming to create.
Learning for Grasping: Machine learning methods have
proven effective for a wide range of perception problems
[64, 22, 38, 59, 3], allowing a perception system to learn
a mapping from some feature set to various visual proper-
ties. Early work by Kamon et al. [31] showed that learning
approaches could also be applied to the problem of grasping
from vision, introducing a learning component to grasp quality
scores.
Recent works have employed richer features and learning
methods, allowing robots to grasp known objects which might
be partially occluded [27] or in an unknown pose [13] as
well as fully novel objects which the system has not seen
before [54]. Here, we will address the latter case. Earlier
work focused on detecting only a single grasping point from
2D partial-view data, using heuristic methods to determine
a gripper pose based on this point. [55]. The use of 3D
data was shown to signiﬁcantly improve these results [56]
thanks to giving direct physical information about the object
in question. With the advent of low-cost RGB-D sensors such
as the Kinect, the use of depth data for robotic grasping has
become ubiquitous.
Several other works attempted to use the learning algorithm
to more fully constrain the detected grasps. Ekvall and Kragic
[15] and Huebner and Kragic [23] used shape-based approxi-
mations as bases for learning algorithms which directly gave
an approach vector. Le et al. [36] treated grasp detection as a
ranking problem over sets of contact points in image space.
Jiang et al. [28] represented a grasp as a 2D oriented rectangle
in image space, with two edges corresponding to the gripper
plates, using surface normals to determine the grasp approach
vector. These approaches allow the detection algorithm to
detect more exactly the gripper pose which should be used
for grasping. In this work, we will follow the rectangle-based
method.
Learning-based approaches have shown impressive results
in grasping novel objects, showing that learning some param-
eters of the detection system can outperform human tuning.
However, these approaches still require a signiﬁcant degree of
hand-engineering in the form of designing good input features.
Other Applications with RGBD Data. Due to the avail-
ability of inexpensive depth sensors, RGB-D data has been a
signiﬁcant research focus in recent years for various robotics
applications. For example, Jiang et al. [30] consider robotic
placement of objects, while Teuliere and Marchand [63] used
RGB-D data for visual servoing. Several works, including
those of Endres et al. [16] and Whelan et al. [66] have ex-
tended and improved Simultaneous Localization and Mapping
(SLAM) for RGB-D data. Object detection and recognition
has been a major focus in research on RGB-D data [11, 33, 7].
Most such works use hand-engineered features such as [52].
The few works that perform feature learning for RGB-D data
[59, 3] largely ignore the multimodal nature of the data, not
distinguishing the color and depth channels. Here, we present
a structured regularization approach which allows us to learn
more robust features for RGB-D and other multimodal data.
B. Deep Learning
Deep learning approaches have demonstrated the ability to
learn useful features directly from data for a wide variety of
tasks. Early work by Hinton and Salakhutdinov [22] showed
that a deep network trained on images of hand-written digits
will learn features corresponding to pen-strokes. Later work
using localized convolutional features [38] showed that these
networks learn features corresponding to object parts when
trained on natural images. This demonstrates that even the
basic features learned by these systems will adapt to the data
given. In fact, these approaches are not restricted to the visual
domain, but rather have been shown to learn useful features for
a wide range of domains, such as audio [39, 41] and natural
language data [12].
Deep Learning for Detection: However, the vast majority
of work in deep learning focuses on classiﬁcation problems.
Only a handful of previous works have applied these methods
to detection problems [45, 37, 9]. For example, Osadchy et al.
[45] and LeCun et al. [37] applied a deep energy-based model
to the problem of face detection, Sermanet et al. [57] applied
a convolutional neural network for pedestrian detection, and
Coates et al. [9] used a deep learning approach to detect text in
images. Girshick et al. [19] used learned convolutional features
over image regions for object detection, while Szegedy et al.
[62] used a multi-scale approach based on deep networks for
the same task.
All these approaches focused on object detection and similar
problems, in which the goal is to ﬁnd a bounding box
which tightly contains the item to be detected, and for each
item, all valid bounding boxes will be similar. However, in
robotic grasp detection, there may be several valid grasps for
an object in different regions, making it more important to
select the one with the highest chance success. In addition,
orientation matters much more to robotic grasp detection, as
most grasps will only be viable for a small subset of the
possible gripper orientations. Our approach to grasp detection
will also generalize across object classes, and even to classes
never seen before by the system, as opposed to the class-
speciﬁc nature of object detection.
Multimodal Deep Learning: Recent works in deep learning
have extended these methods to handle multiple modalities
of input data, such as audio and video [43], text and image
data [61], and even RGB-D data [59, 3]. However, all of these
approaches have fallen into two camps - either learning com-
pletely separate low-level features for each modality [43, 61],
or simply concatenating the modalities [59, 3]. The former
approaches have proven effective for data where the basic
modalities differ signiﬁcantly, such as the aforementioned case
of text and images, while the latter is more effective in cases
where the modalities are more similar, such as RGB-D data.
For some new combinations of modalities and tasks, it
may not be clear which of these approaches will give better
performance. In fact, in the ideal feature set, different features
Fig. 2: Detecting and executing grasps: From left to right: Our system obtains an RGB-D image from a Kinect mounted on the robot,
and searches over a large space of possible grasps, for which some candidates are shown. For each of these, it extracts a set of raw features
corresponding to the color and depth images and surface normals, then uses these as inputs to a deep network which scores each rectangle.
Finally, the top-ranked rectangle is selected and the corresponding grasp is executed using the parameters of the detected rectangle and the
surface normal at its center. Red and green lines correspond to gripper plates, blue in RGB-D features indicates masked-out pixels.
may use different subsets of the modalities. In this work, we
will give a structured regularization method which guides the
learning algorithm to select such subsets, without imposing
hard constraints on network structure.
Structured Learning and Structured Regularization: Sev-
eral approaches have been proposed which attempt to use a
specially-designed regularization function to impose structure
on a set of learned parameters without directly enforcing it.
Jalali et al. [26] used a group regularization function in the
multitask learning setting, where one set of features is used for
multiple tasks. This function applies high-order regularization
separately to particular groups of parameters. Their function
regularized the number of features used for each task in a set of
multi-class classiﬁcation tasks solved by softmax regression.
Intuitively, this encodes the belief that only some subset of
the input features will be useful for each task, but this set of
useful features might vary between tasks.
A few works have also explored the use of structured
regularization in deep learning. The Topographic ICA algo-
rithm [24] is a feature-learning approach that applies a similar
penalty term to feature activations, but not to the weights
themselves. Coates and Ng [8] investigate the problem of
selecting receptive ﬁelds, i.e., subsets of the input features
to be used together in a higher-level feature. The structure
of the network is learned ﬁrst, then ﬁxed before learning the
parameters of the network.
III. D EEP LEARNING FOR GRASP DETECTION :
SYSTEM AND MODEL
In this work, we will present an algorithm for robotic grasp
detection from a single RGB-D view. Our approach will be
based on machine learning, but distinguish itself from previous
approaches by learning not only the weights used to rank
prospective grasps, but also the features used to rank them,
which were previously hand-engineered.
We will do this using deep learning methods, learning a
set of RGB-D features which will be extracted from each
candidate grasp, then used to score that grasp. Our approach
will include a structured multimodal regularization method
which improves the quality of the features learned from
RGB-D data without constraining network structure.
In our system for robotic grasping, as shown in Fig. 2, the
robot ﬁrst obtains an RGB-D image of the scene containing
objects to be grasped. A small deep network is used to score
potential grasps in this image, and a small candidate set of the
top-ranked grasps is provided to a larger deep network, which
yields a single best-ranked grasp.
In this work, we will represent potential grasps using
oriented rectangles in the image plane as seen on the left in
Fig. 2, with one pair of parallel edges corresponding to the
robotic gripper [28]. Each rectangle is thus parameterized by
the X and Y coordinates of its upper-left corner, its width,
height, and orientation in the image plane, giving a ﬁve-
dimensional search space for potential grasps. Grasps will be
ranked based on features extracted from the RGB-D image
region contained inside their corresponding rectangle, aligned
to the gripper plates, as seen in the center of Fig. 2.
To translate a rectangle such as that shown on the right in
Fig. 2 into a gripper pose for grasping we ﬁnd the point with
the minimum depth inside the central third (horizontally) of
the rectangle. We then use the averaged surface normal around
this point to determine the approach vector for the gripper.
The orientation of the detected rectangle is translated to a
rotation around this vector to orient the gripper. We use the
X-Y coordinates of the rectangle center along with the depth
of the closest point to determine a grasping point in the robot’s
coordinate frame. We compute a pre-grasp position by shifting
10 cm back from the grasping point along this approach vector
and position the gripper at this point. We then approach the
object along the approach vector and grasp it.
Using a standard feature learning approach such as sparse
auto-encoder [21], a deep network can be trained for the
problem of grasping rectangle recognition (i.e., does a given
rectangle in image space correspond to a valid robotic grasp?).
Fig. 3:Illustration of our two-stage detection process: Given an image of an object to grasp, a small deep network is used to exhaustively
search potential rectangles, producing a small set of top-ranked rectangles. A larger deep network is then used to ﬁnd the top-ranked rectangle
from these candidates, producing a single optimal grasp for the given object.
Fig. 4: Deep network and auto-encoder: Left: A deep network
with two hidden layers, which transform the input representation,
and a logistic classiﬁer at the top layer, which uses the features
from the second hidden layer to predict the probability of a grasp
being feasible. Right: An auto-encoder, used for pretraining. A set of
weights projects input features to a hidden layer. The same weights
are then used to project these hidden unit outputs to a reconstruction
of the inputs. In the sparse auto-encoder (SAE) algorithm, the hidden
unit activations are also penalized.
However, in a real-world robotic setting, our system needs to
perform detection (i.e., given an image containing an object,
how should the robot grasp it?). This task is signiﬁcantly more
challenging than simple recognition.
Two-stage Cascaded Detection: In order to perform detec-
tion, one naive approach could be to consider each possible
oriented rectangle in the image (perhaps discretized to some
level), and evaluate each rectangle with a deep network
trained for recognition. However, such near-exhaustive search
of possible rectangles (based on positions, sizes, and orienta-
tions) can be quite expensive in practice for real-time robotic
grasping.
Motivated by multi-step cascaded approaches in previous
work [28, 64], we instead take a two-stage approach to
detection: First, we use a reduced feature set to determine
a set of top candidates. Then, we use a larger, more robust
feature set to rank these candidates.
However, these approaches require the design of two sepa-
rate sets of features. In particular, it can be difﬁcult to manually
design a small set of ﬁrst-stage features which is both quick to
compute and robust enough to produce a good set of candidate
detections for the second stage. Using deep learning allows us
to circumvent the costly manual design of features by simply
training networks of two different sizes, using the smaller for
the exhaustive ﬁrst pass, and the larger to re-rank the candidate
detection results.
Model: To detect robotic grasps from the rectangle repre-
sentation, we model the probability of a rectangle G(t), with
features x(t) ∈RN being graspable, using a random variable
ˆy(t) ∈{0,1}which indicates whether or not we predict G(t) to
be graspable. We use a deep network, as shown in Fig. 4-left,
with two layers of sigmoidal hidden units h[1] and h[2], with
K1 and K2 units per layer, respectively. A logistic classiﬁer
over the outputs of the second-layer hidden units then predicts
P(ˆy(t)|x(t); Θ), so chosen because ground-truth graspability is
represented as binary. Each layer ℓ will have a set of weights
W[ℓ] mapping from its inputs to its hidden units, so the param-
eters of our model are Θ = {W[1],W[2],W[3]}. Each hidden
unit forms output by a sigmoid σ(a) = 1/(1 + exp(−a)) over
its weighted input:
h[1](t)
j = σ
(N∑
i=1
x(t)
i W[1]
i,j
)
h[2](t)
j = σ
(K1∑
i=1
h[1](t)
i W[2]
i,j
)
P(ˆy(t) = 1|x(t); Θ) = σ
(K2∑
i=1
h[2](t)
i W[3]
i
)
(1)
A. Inference and Learning
During inference, our goal is to ﬁnd the single grasping
rectangle with the maximum probability of being graspable for
some new object. With G representing a particular grasping
rectangle position, orientation, and size, we ﬁnd this best
rectangle as:
G∗= arg max
G
P(ˆy(t) = 1|φ(G); Θ) (2)
Here, the function φextracts the appropriate input representa-
tion for rectangle G.
During learning, our goal is to learn the parameters Θ that
optimize the recognition accuracy of our system. Here, input
data is given as a set of pairs of features x(t) ∈ RN and
ground-truth labels y(t) ∈{0,1}for t= 1,...,M . As in most
deep learning works, we use a two-phase learning approach.
In the ﬁrst phase, we will use unsupervised feature learning
to initialize the hidden-layer weights W[1] and W[2]. Pre-
training weights this way is critical to avoid overﬁtting. We
will use a variant of a sparse auto-encoder (SAE) [21], as
illustrated in Fig. 4-right. We deﬁne g(h) as a sparsity penalty
function over hidden unit activations, with λ controlling its
weight. With f(W) as a regularization function, weighted
by β, and ˆx(t) as the reconstruction of x(t), SAE solves the
following to initialize hidden-layer weights:
W∗= arg min
W
M∑
t=1
(||ˆx(t) −x(t)||2
2 + λ
K∑
j=1
g(h(t)
j )) + βf(W)
h(t)
j = σ(
N∑
i=1
x(t)
i Wi,j)
ˆx(t)
i =
K∑
j=1
h(t)
j Wi,j (3)
We ﬁrst use this algorithm to initialize W[1] to reconstruct x.
We then ﬁx W[1] and learn W[2] to reconstruct h[1].
During the supervised phase of the learning algorithm, we
then jointly learn classiﬁer weights W[3] and ﬁne-tune hidden
layer weights W[1] and W[2] for recognition. We maximize the
log-likelihood of the data along with regularization penalties
on hidden layer weights:
Θ∗= arg max
Θ
M∑
t=1
log P(ˆy(t) = y(t)|x(t); Θ)
−β1f(W[1]) −β2f(W[2]) (4)
Two-stage Detection Model: During inference for two-stage
detection, we will ﬁrst use a smaller network to produce a set
of the top T rectangles with the highest probability of being
graspable according to network parameters Θ1. We will then
use a larger network with a separate set of parameters Θ2 to
re-rank these T rectangles and obtain a single best one. The
only change to learning for the two-stage model is that these
two sets of parameters are learned separately, using the same
approach.
IV. S YSTEM DETAILS
In this section, we will deﬁne the set of raw features which
our system will use, forming xin the equations above, and how
they are extracted from an RGB-D image. Some examples of
these features are shown in Fig 2.
Our algorithm uses only local information - speciﬁcally, we
extract the RGB-D sub-image contained within each rectangle,
and use this to generate features for that rectangle. This image
is rotated so that its left and right edges correspond to the
gripper plates, and then re-scaled to ﬁt inside the network’s
receptive ﬁeld.
From this 24x24 pixel image, seven channels’ worth of
features are extracted, giving 24x24x7 = 4032 input features.
Fig. 5: Preserving aspect ratio: Left: a pair of sunglasses with
a potential grasping rectangle. Red edges indicate gripper plates.
Center: image taken from the rectangle and rescaled to ﬁt a square
aspect ratio. Right: same image, padded and centered in the receptive
ﬁeld. Blue areas indicate masked-out padding. When rescaled, the
rectangle incorrectly appears graspable. Preserving aspect ratio and
padding allows the rectangle to correctly appear non-graspable.
Fig. 6:Improvement from mask-based scaling: Left: Result with-
out mask-based scaling. Right: Result with mask-based scaling.
The ﬁrst three channels are the image in YUV color space,
used because it represents image intensity and color separately.
The next is simply the depth channel of the image. The last
three are the X, Y , and Z components of surface normals
computed based on the depth channel. These are computed
after the image is aligned to the gripper so that they are always
relative to the gripper plates.
A. Data Pre-Processing
Whitening data is critical for deep learning approaches to
work well, especially in cases such as multimodal data where
the statistics of the input data may vary greatly. While PCA-
based approaches have been shown to be effective [25], they
are difﬁcult to apply in cases such as ours where large portions
of the data may be masked out.
Depth data, in particular, can be difﬁcult to whiten because
the range of values may be very different for different patches
in the image. Thus, we ﬁrst whiten each depth patch indi-
vidually, subtracting the patch-wise mean and dividing by the
patch-wise standard deviation, down to some minimum.
For multimodal data, the statistics of the data for each
modality should match as closely as possible, to avoid learning
features which are biased towards or away from using partic-
ular modes. This is particularly important when regularizing
each modality separately, as in our approach. Thus, we drop
mean values for each feature separately, but scale the data for
each channel by dividing by the standard deviation of all its
features combined.
B. Preserving Aspect Ratio.
It is important for to preserve aspect ratio when feeding
features into the network. This is because distorting image fea-
tures may cause non-graspable rectangles to appear graspable,
as shown in Fig. 5. However, padding with zeros can cause
rectangles with less padding to receive higher graspability
Fig. 7:Three possible models for multimodal deep learning: Left: fully dense model—all visible features are concatenated and modality
information is ignored. Middle: modality-speciﬁc sparse model - separate ﬁrst layer features are trained for each modality. Right: group-sparse
model—a structured regularization term encourages features to use only a subset of the input modes.
scores, as the network will have more nonzero inputs. It is
important to account for this because in many cases the ideal
grasp for an object might be represented by a thin rectangle
which would thus contain many zero values in its receptive
ﬁeld from padding.
To address this problem, we scale up the magnitude of the
available input for each rectangle based on the fraction of
the rectangle which is masked out. In particular, we deﬁne a
multiplicative scaling factor for the inputs from each modality,
based on the fraction of each mode which is masked out, since
each mode may have a different mask.
In the multimodal setting, we assume that the input data xis
known to come from Rdistinct modalities, for example audio
and video data, or depth and RGB data. We deﬁne the modality
matrix S as an RxN binary matrix, where each element Sr,i
indicates membership of visible unit xi in a particular modality
r, such as depth or image intensity. The scaling factor for mode
r is then deﬁned as: Ψ(t)
r = ∑N
i=1 Sr,i/
(∑N
i=1 Sr,iµ(t)
i
)
,
where µ(t)
i is 1 if x(t)
i is masked in, 0 otherwise. The scaling
factor for case i is: ψ(t)
i = ∑R
r=1 Sr,iΨ(t)
r .
We could simply scale up each value of xby its correspond-
ing scale factor when training our model, as x′(t)
i = ψ(t)
i x(t)
i .
However, since our sparse autoencoder penalizes squared error,
scaling x linearly will scale the error for the corresponding
cases quadratically, causing the learning algorithm to lend
increased signiﬁcance to cases where more data is masked out.
Instead, we can use the scaled x′as input to the network, but
penalize reconstruction based on the original x, only scaling
after the squared error has been computed:
W∗= arg min
W
M∑
t=1


N∑
i=1
ψ(t)
i (ˆx(t)
i −x(t)
i )2 + λ
K∑
j=1
g(h(t)
j )


(5)
We redeﬁne the hidden units to use the scaled visible input:
h(t)
j = σ
(N∑
i=1
x′(t)
i Wi,j
)
(6)
This approach is equivalent to adding additional, potentially
fractional, ‘virtual’ visible units to the model based on the
scaling factor for each mode. In practice, we found it necessary
to limit the scaling factor to a maximum of some value c, as
Ψ′(t)
r = min(Ψ(t)
r ,c).
As shown in Table III our mask-based scaling technique at
the visible layer improves grasping results by over 25% for
both metrics. As seen in Figure 6, it removes the network’s
inherent bias towards square rectangles, exhibiting a much
wider range of aspect ratios that more closely matches that
of the ground-truth data.
V. S TRUCTURED REGULARIZATION FOR
FEATURE LEARNING
A naive way of applying feature learning to multimodal
data is to simply take x (as a concatenated vector) as input
to the model described above, ignoring information about
speciﬁc modalities, as seen on the lefthand side of Figure 7.
This approach may either 1) prematurely learn features which
include all modalities, which can lead to overﬁtting, or 2) fail
to learn associations between modalities with very different
underlying statistics.
Instead of concatenating multimodal input as a vector,
Ngiam et al. [43] proposed training a ﬁrst layer representation
for each modality separately, as shown in Figure 7-middle.
This approach makes the assumption that the ideal low-level
features for each modality are purely unimodal, while higher-
layer features are purely multimodal. This approach may work
better for some problems where the modalities have very
different basic representations, such as the video and audio
data (as used in [43]), so that separate ﬁrst layer features
may give better performance. However, for modalities such
as RGB-D data, where the input modes represent different
channels of an image, learning low-level correlations can lead
to more robust features – our experiments in Section VI show
that simply concatenating the input modalities signiﬁcantly
outperforms training separate ﬁrst-layer features for robotic
grasp detection from RGB-D data.
For many problems, it may be difﬁcult to tell which of these
approaches will perform better, and time-consuming to tune
and comparatively evaluate multiple algorithms. In addition,
the ideal feature set for some problems may contain features
(a) Features corresponding to positive grasps.
 (b) Features corresponding to negative grasps.
Fig. 8:Features learned from grasping data: Each feature contains seven channels - from left to right, depth, Y , U, and V image channels,
and X, Y , and Z surface normal components. Vertical edges correspond to gripper plates. Left: eight features with the strong positive
correlations to rectangle graspability. Right: similar, but negative correlations. Group regularization eliminates many modalities from many
of these features, making them more robust.
which use some, but not all, of the input modalities, a case
which neither of these approaches are designed to handle.
To solve these problems, we propose a new algorithm for
feature learning for multimodal data. Our approach incorpo-
rates a structured penalty term into the optimization problem
to be solved during learning. This technique allows the model
to learn correlated features between multiple input modalities,
but regularizes the number of modalities used per feature
(hidden unit), discouraging the model from learning weak
correlations between modalities. With this regularization term,
the algorithm can specify how mode-sparse or mode-dense the
features should be, representing a continuum between the two
extremes outlined above.
Regularization in Deep Learning: In a typical deep learn-
ing model, L2 regularization (i.e., f(W) = ||W||2
2) or L1
regularization (i.e., f(W) = ||W||1) are commonly used in
training (e.g., as speciﬁed in Equations (3) and (4)). These are
often called a “weight cost” (or “weight decay”), and are left
implicit in many works.
Applying regularization is well known to improve the gen-
eralization performance of feature learning algorithms. One
might expect that a simple L1 penalty would eliminate weak
correlations in multimodal features, leading to features which
use only a subset of the modes each. However, we found that in
practice, a value of β large enough to cause this also degraded
the quality of features for the remaining modes and lead to
decreased task performance.
Multimodal Regularization: Structured regularization, such
as in [26], takes a set of groups of weights, and applies
some regularization function (typically high-order) separately
to each group. In our structured multimodal regularization
algorithm, each modality will be used as a regularization group
separately for each hidden unit. For example, a group-wise p-
norm would be applied as:
f(W) =
K∑
j=1
R∑
r=1
(N∑
i=1
Sr,i|Wp
i,j|
)1/p
(7)
where Sr,i is 1 if feature ibelongs to group rand 0 otherwise.
Using a high value of p allows us to penalize higher-valued
weights from each mode to each feature more strongly than
lower-valued ones. This also means that forming a high-valued
weight in a group with other high-valued weights will accrue
a lower additional penalty than doing so for a group with
only low-valued weights. At the limit ( p →∞), this group
regularization becomes equivalent to the inﬁnity (or max)
norm:
f(W) =
K∑
j=1
R∑
r=1
max
i
Sr,i|Wi,j| (8)
which penalizes only the maximum weight from each mode to
each feature. In practice, the inﬁnity norm is not differentiable
and therefore is difﬁcult to apply gradient-based optimization
methods; in this paper, we use the log-sum-exponential as a
differentiable approximation to the max norm.
In experiments, this regularization function produces ﬁrst-
layer weights concentrated in fewer modes per feature. How-
ever, we found that at values of β sufﬁcient to induce the
desired mode-wise sparsity patterns, penalizing the maximum
also had the undesirable side-effect of causing many of the
weights for other modes to saturate at their mode’s maximum,
suggesting that the features were overly constrained. In some
cases, constraining the weights in this manner also caused
the algorithm to learn duplicate (or redundant) features, in
effect scaling up the feature’s contribution to reconstruction to
compensate for its constrained maximum. This is obviously an
undesirable effect, as it reduces the effective size (or diversity)
of the learned feature set.
This suggests that the max-norm may be overly con-
straining. A more desirable sparsity function would penalize
nonzero weight maxima for each mode for each feature
without additional penalty for larger values of these maxima.
We can achieve this effect by applying the L0 norm, which
takes a value of 0 for an input of 0, and 1 otherwise, on top
of the max-norm from above:
f(W) =
K∑
j=1
R∑
r=1
I{(max
i
Sr,i|Wi,j|) >0} (9)
where I is the indicator function, which takes a value of 1
if its argument is true, 0 otherwise. Again, for a gradient-
based method, we used an approximation to the L0 norm,
such as log(1+ x2). This regularization function now encodes
Fig. 9:Example objects from the Cornell grasping dataset: [28].
This dataset contains objects from a large variety of categories.
a direct penalty on the number of modes used for each
weight, without further constraining the weights of modes with
nonzero maxima.
Figure 8 shows features learned from the unsupervised stage
of our group-regularized deep learning algorithm. We discuss
these features, and their implications for robotic grasping, in
Section VII.
VI. E XPERIMENTS
A. Dataset
We used the extended version of the Cornell grasping
dataset for our experiments. This dataset, along with code for
this paper, is available at http://pr.cs.cornell.edu/
deepgrasping. We note that this is an updated version
of the dataset used in [28], containing several more complex
objects, and thus results for their algorithms will be different
from those in [28]. This dataset contains 1035 images of
280 graspable objects, several of which are shown in Fig. 9.
Each image is annotated with several ground-truth positive
and negative grasping rectangles. While the vast majority of
possible rectangles for most objects will be non-graspable, the
dataset contains roughly equal numbers of graspable and non-
graspable rectangles. We will show that this is useful for an
unsupervised learning algorithm, as it allows learning a good
representation for graspable rectangles even from unlabeled
data.
We performed ﬁve-fold cross-validation, and present results
for splits on per image (i.e., the training set and the validation
set do not share the same image) and per object (i.e., the
training set and the validation set do not share any images
from the same object) basis. Hyper-parameters were selected
by validating performance on a separate set of 300 grasps not
used in any of the cross-validation splits.
We take seven 24x24 pixel channels as described in Sec-
tion IV as input, giving 4032 input features to each network.
We trained a deep network with 200 hidden units each at
the ﬁrst and second layers using our learning algorithm as
described in Sections III and V. Training this network took
roughly 30 minutes. For trials involving our two-pass system,
we trained a second network with 50 hidden units at each
layer in the same manner. During inference we performed an
TABLE I: Recognition results for Cornell grasping dataset.
Algorithm Accuracy (%)
Chance 50
Jiang et al. [28] 84.7
Jiang et al. [28] + FPFH 89.6
Sparse AE, separate layer-1 feat. 92.8
Sparse AE 93.7
Sparse AE, group reg. 93.7
exhaustive search using this network, then used the 200-unit
network to re-rank the 100 highest-ranked rectangles found by
the 50-unit network.
B. Baselines
We compare our recognition results in the Cornell grasping
dataset with the features from [28], as well as the combination
of these features and Fast Point Feature Histogram (FPFH)
features [51]. We used a linear SVM for classiﬁcation, which
gave the best results among all other kernels. We also report
chance performance, obtained by randomly selecting a label
in the recognition case, and randomly assigning scores to
rectangles in the detection case.
We also compare our algorithm to other deep learning
approaches. We compare to a network trained only with
standard L1 regularization, and a network trained in a manner
similar to [43], where three separate sets of ﬁrst layer features
are learned for the depth channel, the combination of the Y ,
U, and V channels, and the combination of the X, Y , and Z
surface normal components.
C. Metrics for Detection
For detection, we compare the top-ranked rectangle for
each method with the set of ground-truth rectangles for each
image. We present results using two metrics, the “point” and
“rectangle” metric.
For the point metric, similar to Saxena et al. [55], we com-
pute the center point of the predicted rectangle, and consider
the grasp a success if it is within some distance from at least
one ground-truth rectangle center. We note that this metric
ignores grasp orientation, and therefore might overestimate the
performance of an algorithm for robotic applications.
For the rectangle metric, similar to Jiang et al. [28], let Gbe
the top-ranked grasping rectangle predicted by the algorithm,
and G∗ be a ground-truth rectangle. Any rectangles with
an orientation error of more than 30o from G are rejected.
From the remaining set, we use the common bounding box
evaluation metric of intersection divided by union - i.e.
Area(G∩G∗)/Area(G∪G∗). Since a ground-truth rectangle
can deﬁne a large space of graspable rectangles (e.g., covering
the entire length of a pen), we consider a prediction to be
correct if it scores at least 25% by this metric.
VII. R ESULTS AND DISCUSSION
A. Deep Learning for Robotic Grasp Detection
Figure 8 shows the features learned by the unsupervised
phase of our algorithm which have a high correlation to
Positive
Negative
Fig. 10:Learned 3D depth features: 3D meshes for depth channels of the four features with strongest positive (top) and negative(bottom)
correlations to rectangle graspability. Here X and Y coordinates corresponds to positions in the deep network’s receptive ﬁeld, and Z
coordinates corresponds to weight values to the depth channel for each location. Feature shapes clearly correspond to graspable and non-
graspable structures, respectively.
positive and negative grasping cases. Many of these features
show non-zero weights to the depth channel, indicating that
it learns the correlation of depths to graspability. We can see
that weights to many of the modalities for these features have
been eliminated by our structured regularization approach. In
particular, many of these features lack weights to the U and V
(3rd and 4th) channels, which correspond to color, allowing
the system to be more robust to different-colored objects.
Figure 10 shows 3D meshes for the depth channels of
the four features with the strongest positive and negative
correlations to valid grasps. Even without any supervised infor-
mation, our algorithm was able to learn several features which
correlate strongly to graspable cases and non-graspable cases.
The ﬁrst two positive-correlated features represent handles,
or other cases with a raised region in the center, while the
second two represent circular rims or handles. The negatively-
correlated features represent obviously non-graspable cases,
such as ridges perpendicular to the gripper plane and “valleys”
between the gripper plates. From these features, we can see
that even during unsupervised feature learning, our approach
is able to learn a representation useful for the task at hand,
thanks purely to the fact that the data used is composed of
half graspable and half non-graspable cases.
From Table I, we see that the recognition performance is
signiﬁcantly improved with deep learning methods, improving
9% over the features from [28] and 4.1% over those features
combined with FPFH features. Both L1 and group regulariza-
tion performed similarly for recognition, but training separate
ﬁrst layer features decreased performance slightly. This shows
that learned features, in addition to avoiding hand-design, are
able to improve performance signiﬁcantly over the state of
the art. It demonstrates that a deep network is able to learn
the concept of “graspability” in a way that generalizes to new
objects it hasn’t seen before.
Table II shows that even using any one of the three input
modalities (RGB, depth, or surface normals), our algorithm
is able to learn features which outperform hand-engineered
ones for recognition. Depth gives the highest performance
of any single-mode network. Combining depth and normal
TABLE II:Recognition results for different modalities, for a deep
network pre-trained using SAE.
Modes Accuracy (%)
Chance 50
RGB 90.3
Depth 92.4
Surf. Normals 90.3
Depth + Surf. Normals 92.8
RGB + Depth + Surf. Normals 93.7
information improves results over either alone, indicating that
they give non-redundant information.
The highest accuracy is still obtained by using all the
input modalities. This shows that combining depth and color
information leads to a system which is more robust than
either modality alone. This is due to the fact that some
graspable cases (rims of monochromatic objects, etc.) can only
be detected using depth information, while in others, the depth
channel may be extremely noisy, requiring the use of color
information. From this, we can see that integrating multimodal
information, a major focus of this work, is important in
recognizing good robotic grasps.
Table III shows that the performance gains from deep learn-
ing for recognition carry over to detection, as well. Once mask-
based scaling has been applied, all deep learning approaches
except for training separate ﬁrst-layer features outperform the
hand-engineered features from [28] by up to 13% for the point
metric and 17% for the rectangle metric, while also avoiding
the need to design task-speciﬁc features. Without mask-based
scaling, the system performs poorly, due to the bias illustrated
in Fig. 6. Separate ﬁrst-layer features also give weak detection
performance, indicating that the relative scores assigned by
this form of network are less robust than those learned using
our structured regularization approach.
Using structured multimodal regularization also improves
results over standard L1regularization by up to 1.8%, showing
that our method also learns more robust features than standard
approaches which ignore modality information. Even though
using the ﬁrst-pass network alone underperforms the second-
TABLE III: Detection results for point and rectangle metrics , for
various learning algorithms.
Algorithm Image-wise split Object-wise split
Point Rect Point Rect
Chance 35.9 6.7 35.9 6.7
Jiang et al. [28] 75.3 60.5 74.9 58.3
SAE, no mask-based scaling 62.1 39.9 56.2 35.4
SAE, separate layer-1 feat. 70.3 43.3 70.7 40.0
SAE, L1 reg. 87.2 72.9 88.7 71.4
SAE, struct. reg., 1 st pass only 86.4 70.6 85.2 64.9
SAE, struct. reg., 2 nd pass only 87.5 73.8 87.6 73.2
SAE, struct. reg. two-stage 88.4 73.9 88.1 75.6
Wide gripper
Thin gripper
0 degrees 45 degrees 90 degrees
Fig. 11: Visualization of grasping scores for different grippers:
Red indicates maximum score for a grasp with left gripper plane
centered at each point, blue is similar for the right plate. Best-scoring
rectangle shown in green/yellow.
pass network alone by up to 8.3%, integrating both in our two-
pass system outperforms the solo second-pass network by up
to 2.4%. This shows that the two-pass system improves not
only efﬁciency, but accuracy as well. The performance gains
from multimodal regularization and the two-pass system are
discussed in detail below.
Our system outperforms all baseline approaches by all
metrics except for the point metric in the object-wise split
case. However, we can see that the chance performance is
much higher for the point metric than for the rectangle metric.
This shows that the point metric can overstate performance,
and the rectangle metric is a better indicator of the accuracy
of a grasp detection system.
Adaptability: One important advantage of our detection
system is that we can ﬂexibly specify the constraints of the
gripper in our detection system. This is particularly important
for a robot like Baxter, where different objects might require
different gripper settings to grasp. We can constrain the
detectors to handle this. Figure 11 shows detection scores
for systems constrained based on two different settings of
Baxter’s gripper, one wide and one thin. The implications of
these results for other types of grippers will be discussed in
Section IX.
B. Multimodal Group Regularization
Our group regularization term improves detection accuracy
over simple L1 regularization. The improvement is more
signiﬁcant for the object-wise split than for the image-wise
split because the group regularization helps the network to
avoid overﬁtting, which will tend to occur more when the
learning algorithm is evaluated on unseen objects.
Fig. 12: Improvements from group regularization: Cases where
our group regularization approach produces a viable grasp (shown
in green and yellow), while a network trained only with simple L1
regularization does not (shown in blue and red). Top: RGB image,
bottom: depth channel. Green and blue edges correspond to gripper.
Fig. 13: Improvements from two-stage system: Example cases
where the two-stage system produces a viable grasp (shown in green
and yellow), while the single-stage system does not (shown in blue
and red). Top: RGB image, bottom: depth channel. Green and blue
edges correspond to gripper.
Figure 12 shows typical cases where a network trained using
our group regularization ﬁnds a valid grasp, but a network
trained with L1 regularization does not. In these cases, the
grasp chosen by the L1-regularized network appears valid for
some modalities – the depth channel for the sunglasses and nail
polish bottle, and the RGB channels for the scissors. However,
when all modalities are considered, the grasp is clearly invalid.
The group-regularized network does a better job of combining
information from all modalities and is more robust to noise and
missing data in the depth channel, as seen in these cases.
C. Two-stage Detection System
Using our two-pass system enhanced both computational
performance and accuracy. The number of rectangles the full-
size network needed to evaluate was reduced by roughly a
factor of 1000. Meanwhile, detection performance increased
by up to 2.4% as compared to a single pass with the large-
size network, even though using the small network alone
signiﬁcantly underperforms the larger network. In most cases,
the top 100 rectangles from the ﬁrst pass contained the top-
ranked rectangle from an exhaustive search using the second-
stage network, and thus results were unaffected.
Figure 13 shows some cases where the ﬁrst-stage network
pruned away rectangles corresponding to weak grasps which
might otherwise be chosen by the second-stage network. In
Fig. 14: Robotic experiment objects: Several of the objects used
in experiments, including challenging cases such as an oddly-shaped
RC car controller, a cloth towel, plush cat, and white ice cube tray.
these cases, the grasp chosen by the single-stage system might
be feasible for a robotic gripper, but the rectangle chosen by
the two-stage system represents a grasp which would clearly
be successful.
The two-stage system also signiﬁcantly increases the com-
putational efﬁciency of our detection system. Average infer-
ence time for a MATLAB implementation of the deep network
was reduced from 24.6s/image for an exhaustive search using
the larger network to 13.5s/image using the two-stage system.
VIII. R OBOTIC EXPERIMENTS
In order to evaluate the performance of our algorithms in the
real world, we ran an extensive series of robotic experiments.
To explore the generalizability and effect of the robot on
the success rate of our algorithms, we performed experiments
on two different robotic platforms, a Baxter Research Robot
(“Yogi”) and a PR2 (“Kodiak”).
Baxter: The ﬁrst platform used is our Baxter Research Robot,
which we call “Yogi.” Baxter has two arms with seven degrees
of freedom each and a maximum reach of 104 cm, although we
used only the left arm for these experiments. The end-effector
for this arm is a two-ﬁnger parallel gripper. We augmented the
gripper tips using rubber bands for additional friction. Baxter’s
grippers are interchangable, and we used two settings for these
experiments - a “wide” setting with an open width of 8 cm
and closed width of 4 cm, and a “thin” setting with an open
width of 4 cm and a closed width of 0 cm (completely closed,
gripper tips touching).
To detect grasps, we mounted a Kinect sensor to Yogi’s
head, approximately 1.75 m above the ground. angled down-
wards at roughly a 75o angle towards a table in front of it.
The Kinect gives RGB-D images at a resolution of 640x480
pixels. We calibrated the transformation between the Kinect’s
and Yogi’s coordinate frames by marking four points corre-
sponding to a set of 3D axes, and obtaining the coordinates
of these points in both Kinect’s and Yogi’s frames.
All control for Baxter was done by specifying an end-
effector position and orientation, and using the inverse kine-
matics provided with Baxter to determine a set of joint angles
for this pose. Baxter’s built-in control systems were used to
drive the arm to these new joint angles.
PR2: Our second platform was our PR2 robot, “Kodiak.”
Similar to Baxter, PR2 has two 7-DoF arms with approx-
imately 1 m reach, and we used only the left for these
experiments. PR2’s grippers open to a width of 8 cm, and
are capable of closing completely from that span, so we did
not need to use two settings as with Baxter. We augmented
PR2’s gripper friction with gaffer tape on the ﬁngertips.
For the experiments on PR2, we used the Kinect already
mounted to Kodiak’s head, and used ROS’s built-in function-
ality to obtain 3D locations from that Kinect and transform
these to Kodiak’s body frame for manipulation. Control was
performed using the ee cart stiffness controller [5] with tra-
jectories provided by our own custom MATLAB code.
Experimental Setup: For each experiment, we placed a
single object within a 25 cm x 25 cm square on the ta-
ble, approximately 1.2 m below the mounting point of the
Kinect. This square was chosen to be well-contained within
each robot’s workspace, allowing objects to be reached from
most approach vectors. Object positions and orientations were
varied between trials, although objects were always placed in
conﬁgurations in which at least one viable grasp was visible
and accessible to the robot.
When using Baxter, due to the limited stroke (span from
open to closed) of its gripper, we pre-selected one of the
two gripper settings discussed above for each object. We
constrained the search space as illustrated in Fig. 11 to ﬁnd
grasps for that particular setting.
To detect grasps, we ﬁrst took an RGB-D image from the
Kinect with no objects in the scene as a background image.
The depth channel of this image was used to segment objects
from the scene, and to correct for the slant of the Kinect. Once
an object was segmented, we used our algorithm, as described
above, to obtain a single best-ranked grasping rectangle.
The search space for the ﬁrst-pass network progressed in 15-
degree increments from 15 to 180 degrees (angles larger than
180 being mirror-images of grasps already tested), searching
over 10-pixel increments across the image for the X and Y
coordinates of the upper-left corner of the rectangle. For the
thin gripper setting, rectangle widths and heights from 10 to
40 pixels in 10-pixel increments were searched, while for the
thick setting these ranged from 40 pixels to 100 pixels in
20-pixel increments. In both cases, rectangles taller than they
were wide were ignored. Once a single best-scoring grasp was
detected, we translated it to a robotic grasp consisting of a
grasping point and an approach vector using the rectangle’s
parameters and the surface normal at the rectangle’s center as
described above.
To execute the grasp, we ﬁrst positioned the gripper at
a location 10 cm back from the grasping point along the
approach vector. The gripper was oriented to the approach
vector, and rotated around it based on the orientation of the
detected grasping rectangle.
Since Baxter’s arms are highly compliant, slight impreci-
sions in end-effector positioning are to be expected – we found
that errors of up to 2 cm were typical. Thus, we implemented a
visual servoing system using its hand camera, which provides
TABLE IV: Results for robotic experiments for Baxter, sorted by object category, for a total of 100 trials.
Tr. indicates number of trials, Acc. indicates accuracy (in terms of success percentage.)
Kitchen tools Lab tools Containers Toys Others
Object Tr. Acc. Object Tr. Acc. Object Tr. Acc. Object Tr. Acc. Object Tr. Acc.
Can opener 3 100 Kinect 5 100 Colored cereal box 3 100 Plastic whale 4 75 Electric shaver 3 100
Knife 3 100 Wire bundle 3 100 White cereal box 4 50 Plastic elephant 4 100 Umbrella 4 75
Brush 3 100 Mouse 3 100 Cap-shaped bowl 3 100 Plush cat 4 75 Desk lamp 3 100
Tongs 3 100 Hot glue gun 3 67 Coffee mug 3 100 RC controller 3 67 Remote control 5 100
Towel 3 100 Quad-rotor 4 75 Ice cube tray 3 100 XBox controller 4 50 Metal bookend 3 33
Grater 3 100 Duct tape roll 4 100 Martini glass 3 0 Plastic frog 3 67 Glove 3 100
Average 100 Average 90 Average 75 Average 72 Average 85
Overall 84
TABLE V: Results for robotic experiments for PR2, sorted by object category, for a total of 100 trials.
Tr. indicates number of trials, Acc. indicates accuracy (in terms of success percentage.)
Kitchen tools Lab tools Containers Toys Others
Object Tr. Acc. Object Tr. Acc. Object Tr. Acc. Object Tr. Acc. Object Tr. Acc.
Can opener 3 100 Kinect 5 100 Colored cereal box 3 100 Plastic whale 4 75 Electric shaver 3 100
Knife 3 100 Wire bundle 3 100 White cereal box 4 100 Plastic elephant 4 100 Umbrella 4 100
Brush 3 100 Mouse 3 100 Cap-shaped bowl 3 100 Plush cat 4 100 Desk lamp 3 100
Tongs 3 100 Hot glue gun 3 67 Coffee mug 3 100 RC controller 3 67 Remote control 5 100
Towel 3 100 Quad-rotor 4 100 Ice cube tray 3 100 XBox controller 4 25 Metal bookend 3 67
Grater 3 100 Duct tape roll 4 100 Martini glass 3 0 Plastic frog 3 67 Glove 3 100
Average 100 Average 95 Average 83 Average 72 Average 95
Overall 89
Fig. 15:Robots executing grasps: Our robots grasping several objects from the experimental dataset. Top row: Baxter grasping a quad-rotor
casing, coffee mug, ice cube tray, knife, and electric shaver. Middle row: Baxter grasping a desk lamp, cheese grater, umbrella, cloth towel,
and hot glue gun. Bottom row: PR2 grasping a plush cat, RC car controller, cereal box, toy elephant, and glove.
RGB images at a resolution of 320x200 pixels. We used color
segmentation to separate the object from the background, and
used its lateral position in image space to drive Yogi’s end-
effector to center the object. We did not implement visual
servoing for PR2 because its gripper positioning was found to
be precise to within 0.5 cm.
After visual servoing was completed, we drove the gripper
14 cm forwards from its current position along the approach
vector, so that the grasping point was well-contained within
it. We then closed the gripper, grasping the object, and moved
it 30 cm upwards. A grasp was determined to be successful if
it was sufﬁcient to lift the object and hold it for one second.
Objects to be Grasped: For our robotic experiments, we
collected a diverse set of 35 objects within a size of .3 m x .3
m x .3 m and weighing at most 2.5 kg (although most were
less than 1 kg) from our ofﬁces, homes, and lab. Many of them
are shown in Fig. 14. Most of these objects were not present
in the training dataset, and thus were completely new to the
grasp detection algorithm.
Due to the physical limitations of the robots’ grippers,
we found that ﬁve of these objects were not graspable even
when given a hand-chosen grasp. The small pair of pliers
was too low to the table to grip properly. The spray paint
can was too smooth for the gripper to get enough friction
to lift it. The weight of the hammer was too imbalanced,
causing the hammer to rotate and slip out of the gripper when
grasped. Similar problems were encountered with the bicycle
U-lock. The bevel spatula’s handle was too close to the thin-
set size of Baxter’s gripper, so that we could not position
it precisely enough to grasp it reliably. We did not consider
these objects for purposes of our experimental results, since
our focus was on evaluating the performance of our grasp
detection algorithm.
Results: Table IV shows the results of our robotic experiments
on Baxter for the remaining 30 objects, a total of 100 trials.
Using our algorithm, Yogi was able to successfully execute a
grasp in 84% of the trials. Figure 15 shows Yogi executing
several of these grasps. In 8% of the trials, our algorithm
detected a valid grasp which was not executed correctly by
Yogi. Thus, we were able to successfully detect a good grasp
in 92% of the trials. Video of some of these trials is available
at http://pr.cs.cornell.edu/deepgrasping.
PR2 yielded a higher success rate as seen in Table V,
succeeding in 89% of trials. This is largely due to the much
wider span of PR2’s gripper from open to closed and its ability
to fully close from its widest position, as well as PR2’s ability
to apply a larger gripping force. Some speciﬁc instances where
PR2 and Baxter’s performance differed are discussed below.
For comparison purposes, we ran a small set of control
experiments for 16 of the objects in the dataset. The control
algorithm simply returned a ﬁxed-size rectangle centered at the
object’s center of mass, as determined by depth segmentation
from the background. The rectangle was aligned so that the
gripper plates ran parallel to the object’s principal axis. This
algorithm was only successful in 31% of cases, signiﬁcantly
underperforming our system.
On Baxter, our algorithm sometimes detected a grasp which
was not realizable by the current setting of its gripper, but
might be executable by others. For example, our algorithm
detected grasps across the leg of the plush cat, and the region
between the handle and body of the umbrella, both too thin
for the wide setting of Baxter’s gripper to grasp since it has
a minimum span of 4 cm. Since PR2’s gripper can close
completely from any position, it did not encounter these issues
and thus achieved a 100% success rate for both these objects.
The XBox controller proved to be a very difﬁcult object for
either robot to grasp. From a top-down angle, there is only a
small space of viable grasps with a span of less than 8 cm, but
many which have either a slightly larger span (making them
non-realizable by either gripper), or are subtly non-viable (e.g.
grasps across the two “handles,” which tend to slip off.) All
viable grasps are very near to the 8 cm span of both grippers,
meaning that even slight imprecision in positioning can lead to
failure. Due to this, Baxter achieved a higher success rate for
the XBox controller thanks to visual servoing, succeeding in
50% of cases as compared to the 25% success rate for PR2.
Our algorithm was able to consistently detect and execute
valid grasps for a red cereal box, but had some failures on a
white and yellow one. This is because the background for all
objects in the dataset is white, leading the algorithm to learn
features relating white areas at the edges of the gripper region
to graspable cases. However, it was able to detect and execute
correct grasps for an all-white ice cube tray, and so does not
fail for all white objects. This could be remedied by extending
the dataset to include cases with different background colors.
Interestingly, even though the parameters of grasps detected
for the white box were similar for PR2 and Baxter, PR2 was
able to succeed in every case while Baxter succeeded only
half the time. This is because PR2’s increased gripper strength
allowed it to execute grasps across corners of the box, crushing
it slightly in the process.
Other failures were due to the limitations of the Kinect
sensor. We were never able to properly grasp the martini glass
because its glossy ﬁnish prevented Kinect from returning any
depth estimates for it. Even if a valid grasp were detected
using color information only, there was no way to infer a
proper grasping position without depth information. Grasps
for the metal bookend failed for similar reasons, but it was
not as glossy as the martini glass, and gave enough returns
for some to succeed.
However, our algorithm also had many noteworthy suc-
cesses. It was able to consistently detect and execute grasps for
a crumpled cloth towel, a complex and irregular case which
bore little resemblance to any object in the dataset. It was also
able to ﬁnd and grasp the rims of objects such as the plastic
baseball cap and coffee mug, cases where there is little visual
distinction between the rim and body of the object. These
objects underscore the importance of the depth channel for
robotic grasping, as none of these grasps would be detectable
without depth information.
Our algorithm was also able to successfully detect and
execute many grasps for which the approach vector was non-
vertical. The grasps shown for the coffee mug, desk lamp,
cereal box, RC car controller, and toy elephant shown in
Fig. 15 were all executed by aligning the gripper to such an
approach vector. Indeed, many of these grasps may have failed
had the gripper been aligned vertically. This shows that our
algorithm is not restricted to detecting top-down grasps, but
rather encodes a more general notion of graspability which
can be applied to grasps from many angles, albeit within the
constraints of visibility from a single-view perspective.
While a few failures occurred, our algorithm still achieved
a high rate of accuracy for other oddly-shaped objects such
as the quad-rotor casing, RC car controller, and glue gun.
For objects with clearly deﬁned handles, such as the cheese
grater, kitchen tongs, can opener, and knife, our algorithm was
able to detect and execute successful grasps in every trial,
showing that there is a wide range of objects which it can
grasp extremely consistently.
IX. D ISCUSSION AND FUTURE WORK
Our algorithm focuses on the problem of grasp detection for
a two-ﬁngered parallel-plate style gripper. It would be directly
applicable to other grippers with ﬁxed conﬁgurations, simply
requiring new training data labeled with grasps for the gripper
in question. Our system would allow even the basic features
used for grasp detection to adapt to the gripper. This might be
useful in cases such as jamming grippers [29], or two-ﬁngered
grippers with differently-shaped contact surfaces, which might
require different features to determine a graspable area.
Our detection algorithm does not directly address the prob-
lem of 3D orientation of the gripper – this orientation is
determined only after an optimal rectangle has been detected,
orienting the grasp based on the object’s surface normals.
However, just as our approach here considers aligns a 2D
feature window to the gripper, an extension of this work might
align a 3D window – using voxels, rather than pixels, as its
basic unit of representation for input features to the network.
This would allow the system to search across the full 6-DoF
3D pose of the gripper, while still leveraging the power of
feature learning.
Our system gives only a gripper pose as output, but multi-
ﬁngered reconﬁgurable hands also require a conﬁguration of
the ﬁngers in order to grasp an object. In this case, our
algorithm could be used as a heuristic to ﬁnd one or more
locations likely to be graspable (similar to the ﬁrst pass in our
two-pass system), greatly reducing the search space needed to
ﬁnd an optimal gripper conﬁguration.
Our algorithm also depends only on local features to de-
termine grasping locations. However, many household objects
may have some areas which are strongly preferable to grasp
over others - for example, a knife might be graspable by
the blade, or a hot glue gun by the barrel, but both should
actually be grasped by their respective handles. Since these
regions are more likely to be labeled as graspable in the
data, our system already weakly encodes this, but some may
not be readily distinguishable using only local information.
Adding a term modeling the probability of each region of
the image being a semantically-appropriate area to grasp the
object would allow us to incorporate this information. This
term could be computed once for the entire image, then added
to each local detection score, keeping detection efﬁcient.
In this work, our visual-servoing algorithm was purely
heuristic, simply attempting to center the segmented object
underneath the hand camera. However, in future work, a simi-
lar feature-learning approach might be applied to hand camera
images of graspable and non-graspable regions, improving the
visual servoing system’s ability to ﬁne-tune gripper position
to ensure a good grasp.
Many robotics problems require the use of perceptual infor-
mation, but can be difﬁcult and time-consuming to engineer
good features for, particularly when using RGB-D data. In
future work, our approach could be extended to a wide range
of such problems. Our system could easily be applied to
other detection problems such as object detection or obstacle
detection. However, it could also be adapted to other similar
problems, such as object tracking and visual servoing.
Multimodal data has become extremely important for
robotics, due both to the advent of new sensors such as the
Kinect and the application of robots to more challenging tasks
which require multiple modalities of information to perform
well. However, it can be very difﬁcult to design features which
do a good job of integrating many modalities. While our
work focuses on color, depth, and surface normals as input
modes, our structured multimodal regularization algorithm
might also be applied to others. This approach could improve
performance while allowing roboticists to focus on other
engineering challenges.
X. C ONCLUSIONS
We presented a system for detecting robotic grasps from
RGB-D data using a deep learning approach. Our method
has several advantages over current state-of-the-art methods.
First, using deep learning allows us to avoid hand-engineering
features, learning them instead. Second, our results show that
deep learning methods signiﬁcantly outperform even well-
designed hand-engineered features from previous work.
We also presented a novel feature learning algorithm for
multimodal data based on group regularization. In extensive
experiments, we demonstrated that this algorithm produces
better features for robotic grasp detection than existing deep
learning approaches to multimodal data. Our experiments and
results, both ofﬂine and on real robotic platforms, show that
our two-stage deep learning system with group regularization
is capable of robustly detecting grasps for a wide range of
objects, even those previously unseen by the system.
ACKNOWLEDGEMENTS
We would like to thank Yun Jiang and Marcus Lim for
useful discussions and help with baseline experiments. This
research was funded in part by ARO award W911NF-12-1-
0267, Microsoft Faculty Fellowship and NSF CAREER Award
(Saxena), and Google Faculty Research Award (Lee).
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Peer review:
[Review]:
Summary: this paper uses the common 2-step procedure to first eliminate most of unlikely detection windows (high recall), then use a network with higher capacity for better discrimination (high precision). Deep learning (in the unsupervised sense) helps having features optimized for each of these 2 different tasks, adapt them for different situations (different robotics grippers) and beat hand-designed features for detection of graspable areas, using a mixture of inputs (depth + rgb + xyz).

Novelty: deep learning for detection is not as uncommon as the authors suggest (pedestrians detection by [4] and imagenet 2012 detection challenge by Krizhevsky), however its application to robotics grasping detection is indeed novel. And detecting rotations (optimal grasping detection) while not completely novel is not extremely common.

Quality: the experiments are well conducted (e.g. proper 5-fold cross validation).

Pros:
- Deep learning successfully demonstrated in a new domain.
- Goes beyond the simpler task of classification.
- Unsupervised learning itself clearly learns interesting 3D features of graspable areas versus non-graspable ones.
- Demonstrates superior results to hand-coded features and automatic adaptability to different grippers.
- The 2-pass shows improvements in quality and ~2x speedup.

Cons:
- Even though networks are fairly small, the system is still far from realtime. Maybe explaining what the current bottlenecks are and further work would be interesting. Maybe you want to use convolutional networks to speed-up detection (no need to recompute each window's features, a lot of them are shared in a detection setting).