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Title

Safety-optimized Strategy for Grasp Detection in
High-clutter Scenarios

Author(s)Li, Chenghao; Zhou, Peiwen; Chong, Nak Young

Citation

2024 21st International Conference on Ubiquitous
Robots (UR): 192-197

Issue Date

2024-07-26

Type

Conference Paper

Text version

author

URL

http://hdl.handle.net/10119/19336

RightsThis is the author's version of the work. Copyright(C) 2024 IEEE. 2024 21st International Conference
on Ubiquitous Robots (UR), New York, NY, USA,192-197. DOI:
https://doi.org/10.1109/UR61395.2024.10597484.
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Description

2024 21st International Conference on Ubiquitous
Robots (UR), New York, NY, USA, June 24-27, 2024

Japan Advanced Institute of Science and Technology

Safety-optimized Strategy for Grasp Detection in High-clutter Scenarios
Chenghao Li, Peiwen Zhou, and Nak Young Chong

Abstract— The detection accuracy and speed of grasp detection models on benchmarks are the focal points of concern in
the robotic grasping community. Especially in a collaborative
robot setting, the safety of the model is an essential aspectthat cannot be overlooked. In this paper, we explore how toenhance the safety of grasp detection models in autonomous
vision-guided grasping. Specifically, we propose a simple yetpractical Safety-optimized Strategy, which consists of two parts.
The first part involves depth prioritization, optimizing the grasp
sequence from top to bottom based on the order of depth values,
which can mitigate the issue of grasp collisions that may arise
when the depth value of the object with the highest grasp quality
is significantly higher than that of other objects in high-clutter
scenarios. The second part is false-positive protection, where we
introduce the robust ArUco marker as the lowest grasp priority.
The marker is fixed at certain positions within the camera’s
field of view, enabling the robot to halt its movement, thereby
restraining the robot from grasping objects that should not be
grasped. Once the marker disappears, the robot can resume
its operations. We validate our method through real grasping
experiments with a parallel-jaw gripper and an industrial
robotic arm, demonstrating its effectiveness in high-clutter
scenarios.

I. INTRODUCTION
The integration of vision-guided robot grasping technology is crucial for enabling robots to effectively interact withthe real world. Traditional visual grasping methods, reliant
on manually extracting object features, often face challenges
in adapting to dynamically changing unstructured scenarios
due to the simplicity of the extracted features. In recent years,
the emergence of deep learning, particularly represented by
Convolutional Neural Networks (CNNs), has played a pivotal
role in advancing computer vision. Unlike artificial features,
CNNs leverage a multi-level structure to learn featuresfrom extensive data, capturing varying levels of relationships from simple to complex. This characteristic allows forsuperior feature expression. Consequently, researchers haveincreasingly applied CNNs to visual grasping, exemplified bymethods such as planar grasping representation based grasp
detection [1], [2], [3]. A typical planar grasp representationencompasses parameters such as grasp point, angle, andwidth. Saxena et al. [4] successfully employed supervised
learning to predict grasp points from images, extending theirapproach effectively to novel objects. Le et al. [5] proposed a
representation using a pair of points to depict grasping, while
Jiang et al. [6] streamlined the 7-dimensional gripper configuration in a real environment to a 5-dimensional rectangle
This work was supported by JSPS KAKENHI Grant Number
JP23K03756.
Authors are with the School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, Japan{chenghao.li, s2310071, nakyoung}@jaist.ac.jp.

grasping representation (x, y, w, h, θ), which is widely usedin later research. Here, (x, y) denotes the grasp center, andw, h, and θ represent the width, height, and angle relative
to the horizontal direction, respectively.
Although these grasp detection methods have achieved
good detection speed and accuracy on benchmarks such as
the Cornell dataset, they overlook some safety issues of grasp
detection models in practical grasping applications. In thiswork, we mainly explore two safety issues. The first safetyissue is grasping collisions caused by significant differencesin depth values of adjacent objects in high-clutter scenarios.
Grasp detection models typically choose to grasp the highestquality object. When the depth value of the object is muchgreater than that of adjacent objects, and the model predicts
a graspable width that spans adjacent objects, the robot
may collide with adjacent objects when grasping the chosen
object. The second safety issue is false-positive detection in
grasp detection, as grasp detection models can generalize tounknown objects, leading them to detect objects that should
not be grasped. For example, in situations where humanassistance is required, a person’s hand may appear in the
robot’s grasping field of view. If the grasp detection model
mistakenly detects the human hand, it can result in a situationthat threatens human safety. To address these two issues,
we propose a simple yet practical Safety-optimized Strategy,
which enhances the safety of the grasp detection model byusing depth prioritization and false-positive protection. Ourcontributions are as follows:
1) We reveal two safety issues of the grasp detection
model: false-positive detection and collisions in highclutter scenarios.
2) We propose a simple yet practical Safety-optimized
Strategy that enhances the safety of the grasp detection
model during grasping operations. And can be serve asa baseline for future research.
3) We validate the effectiveness of our proposed method
through real grasping experiments in high-clutter scenarios.
II. R ELATED W ORKGrasp detection can be classified into two categories
based on the diverse modalities of input visual information.
The first category involves the use of unimodal data forgrasp detection. Johns et al. [7] employed simulated depth
images to predict grasps, refining the optimal grasp througha grasp uncertainty function. Morrison et al. [1] proposed a
generative grasp CNN architecture that pixel-wise generates
grasps from depth images, effectively addressing challenges
related to discrete sampling and computational complexity.

Another recent approach [8] exclusively relied on RGB data,
presenting a grasp detection model based on the CSP-ResNetarchitecture, incorporating multiple residual structures withskip connections. The second category encompasses grasp
detection using multimodal data. Wang et al. [9] introduced
a novel robot grasp detection system, mapping pairs of RGBD images of novel objects to the optimal grasping pose of arobotic gripper. Jiang et al. [6] utilized RGB-D images forgrasp inference through a two-step learning process. Lenz etal. [10] adopted a two-step approach with a deep learning
architecture, encountering challenges in predicting optimal
grasps for diverse object types. Yan et al. [11] employeda point cloud prediction network for grasp generation, involving initial data preprocessing to obtain color, depth, andmasked images. Subsequently, a 3D point cloud of the target
object was generated and fed into a pivotal network to predicta grasp. Chu et al. [12] proposed a novel architecture capableof simultaneously predicting multiple grasps for multiple
objects. Ogas et al. [13] discussed a robotic grasping method
combining ConvNet for object recognition and a grasping
method for objects with known parameters. Kumra et al. [14]introduced a Deep CNN architecture using residual layers
for predicting robust grasps, highlighting the advantages ofa deeper network with residual layers. Asif et al. [15] presented EnsembleNet, a consolidated framework generating
four grasp representations and synthesizing them to produce
grasp scores, ultimately selecting the highest-scoring grasp.
Kumra et al. [16] also proposed GR-ConvNet, a Generative
Residual Convolutional Neural Network model for real-time
generation of robust antipodal grasps from n-channel input.
Although these methods have achieved superior detection accuracy and speed through information from different
modalities and diverse network structures, they all overlooked how to enhance the algorithmic architecture forsafety-critical real-world applications.
III. P ROPOSED M ETHOD
In this section, we elaborate on our Safety-optimized
Strategy. In the first part, we discussed how to prevent
grasping collisions in high-clutter scenes by implementing
depth prioritization. The second part focuses on Falsepositive Protection, where we explain how to reduce
false-positive detections in the grasping detection modelby introducing ArUco markers and altering the perceptual
priorities of the visual system. The final part outlines theoverall implementation process of the Strategy.

typically considered as w/2 (the model does not learn thisparameter). However, these parameters in image space are
not sufficient for real grasping and often require multiple
transformations. The parameters of width and θ in image
space can directly be mapped to the gripper’s width androtation angle relative to the horizontal direction. Usually,
only the position information (x, y) needs to be transformed.
Specifically, it is necessary to first convert (x, y) and depth
information to the depth camera coordinate system (with thehelp of camera intrinsic parameters), then transform from thedepth coordinate system to the robotic arm’s base coordinate
system (using the transformation matrix obtained from handeye calibration), and finally, through inverse kinematics andforward kinematics, convert the grasp parameters in the
robotic arm’s base coordinate system into the rotational
degrees of the individual joints to achieve grasping.
However, in more complex scenarios, such as high-clutter
scenarios where various objects are densely piled up, thedepth value corresponding to the optimal graspable object
may be significantly greater than the depth values of adjacent objects. Additionally, if the predicted graspable widthcorresponding to the optimal grasp point is too wide andextends into adjacent objects, it can lead to collisions with
the neighboring objects during grasping. As shown in Fig.
1. (b), the dinosaur model is surrounded by many volcano
models higher than the dinosaur, and the predicted graspable
width extends onto the volcano. This depth difference causes
the gripper to collide with the surrounding volcano models
during grasping. To mitigate the impact of this issue, we
employ a simple yet practical depth prioritization approach.
Specifically, we extract the top 10 optimal grasp points predicted by the grasp detection model and rearrange their grasp
priority based on the depth values. Here, we set the priority
to be based on the ascending order of depth values (i.e., topdown grasping logic). In addition, considering issues such as
object reflection and background interference in real scenes,
as well as errors caused by the depth camera itself (suchas depth holes), the depth information being excessively
large or small, leading to robot collisions, we optimize depth
prioritization by defining a safe depth range to filter out
grasp points with depth values not meeting the requirements,
thereby controlling the robot’s operation within a certain
height range.

A. Depth Prioritization
Grasp detection models [14], [15], [16] typically choose
to grasp the object with the highest graspable quality basedon the acquired images (such as RGB-D, RGB, or Depth
images). As shown in Fig. 1. (a), which represents thepredicted grasping box by the grasp detection model ina single-object scene. The coordinates (x, y) denote theoptimal grasp point for the dinosaur model in the image,
and width and θ represent the graspable width and height,

Fig. 1. Grasp detection results in different scenarios. (a) indicates a single
object scenario, (b) indicates a high-clutter scenario. In the (b) scenario,
the grasp box easily spans over adjacent objects, leading to collisions withother objects when attempting to grasp the target object.

Fig. 2.

Some ArUco markers with different appearances.

Fig. 3. Robot grasping experimental platform (a) and grasping objects (b).

B. False-positive Protection
The grasp detection model can generalize well to unknown
objects, however this generality is uncontrollable, which isarbitrary. If the model generalized to an unknown object thatshould not be grasped (such as a human hand), then thisgenerality becomes a safety concern in practical applications.
We conducted human hand detection tests with the GRConvNet [16] grasp detection model in different locations
and scenarios (detailed in the experiments part). Since humanhands in the camera’s field of view tend to be at a higher
height when manual assistance to the robot in some special
situations, they are usually not obstructed by other objects
and are fully exposed in the camera’s field of view. We
followed this characteristic during testing. From Fig. 5, it canbe observed that GR-ConvNet [16] has a high probability ofdetecting human hands in different positions and scenarios(i.e., the optimal grasping point is on the human hand).
Assuming that a human hand stays in a certain position forsome time, the robot will immediately grasp the recognizedhuman hand after completing the previous grasp, which may
causes physical harm to human.
ArUco markers [17] (as shown in Fig. 2) have strongrobustness and stability and are widely used in robot handeye calibration, UAV landing assistance, path planning, etc.
In this paper, we also introduce ArUco markers into real
grasping to reduce the false-positive detection of the grasp
detection model. Specifically, we propose False-positive

Algorithm 1 Safety-optimized Strategy
1: Input: original points {P1 , P2 , P3 , ..., Pn }2: Output: optimized points {Pt , Pt+1 , Pt+2 , ..., Pk }3: for i = 1, 2, 3, . . . , n do
4:
if ArU co is T rue then5:
return None6:else if Di ∈/ [Dmin , Dmax ] then7:filter Pi , k ← n − 18:else if Di ∈ [Dmin , Dmax ] then9:save Pi10:
end if
11: end for
12: for j = 1, 2, 3, . . . , k do
13:
Pc ← Pj , Dc ← Dj , t ← j − 1
14:while t ≥ 0 and Dt > Dc do
15:
Dt+1 ← Dt , Pt+1 ← Pt , t ← t − 1
16:
end while17:
Dt+1 ← Dc , Pt+1 ← Dc
18: end for
19: return {Pt , Pt+1 , Pt+2 , ..., Pk }Protection, embedding the recognition algorithm of ArUco
markers into the entire grasping visual system and restricting
the recognition of ArUco markers by the grasping visualsystem as the highest priority (higher than the grasp
detection model) and the grasping of ArUco markers as thelowest priority. In other words, during the grasping process,if an ArUco marker or an object with an ArUco marker
appears, the robot will stop moving, thus protecting humanhands during manual assistance. When the ArUco marker
disappears, the robot can resume its operations, that is, therunning of the code will not be interrupted.
C. The overall process of the Safety-optimized Strategy
The overall process of the Safety-optimized Strategy isshown in Algorithm 1. {P1 , P2 , P3 , ..., Pn } represents the detected top-n graspable points from the grasp detection modelin one frame. Firstly, determine whether the ArUco marker
is recognized. If so, return a null value, meaning the robot
will stop (the code will not stop), until the ArUco marker
disappears, and then the robot will resume task execution. If
the ArUco marker is not recognized, determine whether thegraspable point is within the safe depth range [Dmin , Dmax ].
If so, save this point, otherwise, filter this point. Finally, thefiltered graspable points are sorted according to the descending order of the depth value, that is, {Pt , Pt+1 , Pt+2 , ..., Pk }.
Due to our focus on our optimization methods, we do notelaborate extensively on the existing grasp detection models
and the recognition algorithms for ArUco markers. Detailed
information and open-source code can be referred to in [1],[16], [17].
IV. E XPERIMENTS
In this section, we validated the effectiveness of our
proposed method through experiments. Firstly, we introduced

the experimental setup including the experimental equipment
and scenarios. Then, we validated the effectiveness of Depth
Prioritization in the Safety-optimized Strategy under highclutter scenarios. Finally, we validated the effectiveness ofFalse-positive Protection in the Safety-optimized Strategy
under high-clutter scenarios.

the grasping performance of our method (second row) in
high-clutter scenarios (DPG). As depicted in the figures, ourmethod successfully alleviates the issue of grasp collisions
that occur when the depth value of the object with the highest
grasp quality is substantially higher than that of other objects.
In other words, it enables the system to prioritize grasping
nearby objects with lower-depth values first.

A. Experiments Settings

C. Effectiveness of False-positive Protection

We used a combination of the Cornell Grasp Dataset [10]and the OCID Grasp Dataset [18] as our dataset and opted for
GR-ConvNet (RGB-D version) [16] as the grasp detection
model. The training took place on a single NVIDIA RTX
4070Ti GPU with 12 GB of memory. The computer system
was running Ubuntu 22.04, and we utilized PyTorch 2.1.2
with CUDA 12.1 as the deep learning framework. Following
the training parameters outlined in GR-ConvNet [16], we
randomly shuffled the entire dataset, allocating 90% fortraining and 10% for testing before model training. During
the training process, the data was uniformly cropped to
224×224, and data augmentation techniques such as randomzoom and random rotation were applied. Since our primary
focus was on real grasping scenarios, we directly used thetrained GR-ConvNet with the highest detection accuracy toevaluate the effectiveness of our Safety-optimized Strategy
in grasping.
Our overall grasping system is illustrated in Fig. 3. (a),primarily consisting of one Intel RealSense D415, one
Xarm5 industrial robot, and one Xarm parallel-jaw gripper.
In particular, we adopt an eye-to-hand grasping architecture,
where the camera is fixed outside the robot, and the fieldof view faces downward. Fig. 3. (b) illustrates the objects
utilized in our grasping experiments, comprising a total of
10 different types. In these experiments, we combine these
10 objects to create 10 distinct high-clutter scenarios.

In validating the effectiveness of False-positive Protection,
we primarily examined the False-positive Detection Rate
(FPD-R) of the GR-ConvNet and False-positive Protection
Rate (FPP-R) of our method in various scenarios. The Falsepositive Detection Rate indicates the rate of detecting ahuman hand, whereas the False-positive Protection Ratesignifies the rate at which the system fails to detect a humanhand. During the experiments, the human hand moved todifferent positions and maintained various poses in eachscenario, with 10 tests conducted for each scenario.
The experimental results are presented in Table II. The
False-positive Detection Rate (FPD-R) of the GR-ConvNet
and the False-positive Protection Rate (FPP-R) of our
method reached 84% and 83%, respectively. This indicates that although the GR-ConvNet easily detects human hands, the integration of our method helps alleviate this issue to some extent. We also visualize our experimental results in this section, as shown in Fig. 5.
The first row represents the False-positive detection (FPD)
results of the GR-ConvNet, and the second row represents the False-positive protection (FPP) results of our
method. It can be observed from the figure that when
ArUco markers appear in the scene, the detection of humanhands by the grasp detection model can be suppressed.
Video is presented at: https://www.youtube.com/channel/UCnJesbpK2jbigNBmZTIwjg.

B. Effectiveness of Depth Prioritization
To demonstrate the effectiveness of Depth Prioritization,
we initially tested the Grasping Collision Rate (GC-R) andGrasping Success Accuracy (G-Acc) of the GR-ConvNet
across various scenarios. Subsequently, we evaluated theGrasping Collision Rate after integrating Depth Prioritization(DPGC-R) and the Grasping Success Accuracy with Depth
Prioritization (DPG-Acc). The Grasping Success Rate here
indicates how many of the 10 objects were successfully
grasped in each scene, while the Grasping Collision Rate signifies whether objects not successfully grasped experienced
collisions.
The experimental results are shown in Table I. The Grasping Collision Rate (GC-R) and Grasping Success Accuracy(G-Acc) of the GR-ConvNet reached 36% and 58%, respectively. After adding Depth Prioritization, the Grasping
Collision Rate (GC-R) dropped to 24%, and the Grasping
Success Accuracy (G-Acc) rose to 78%, which validated theeffectiveness of Depth Prioritization. Furthermore, in Fig. 4,
we demonstrate the occurrence of grasp collisions (GC) inhigh-clutter scenarios using the GR-ConvNet (first row) and

V. C ONCLUSION
This paper proposes a Safety-optimized Strategy from theperspective of safety in grasping. It is divided into two parts.
The first part is depth prioritization, which optimizes thegrasp sequence from top to bottom based on the order of
depth values. This approach helps mitigate the issue of grasp
collisions that may occur when the depth value of the object
with the highest grasp quality is significantly higher than thatof other objects in high-clutter scenarios. The second part isfalse-positive protection, where robust ArUco markers are
introduced as the lowest grasp priority and can help limit
human-safety threatening grasping caused by false-positive
detection. Finally, we validate the effectiveness of depth prioritization and false-positive protection through experiments
conducted in real high-clutter grasping scenarios.
Limitation and Future Works: Since this work is still in
its early stages, many parts need improvement. For instance,
depth prioritization mainly focuses on the post-processing
stage of the grasp detection model, and internal structuraldefects of the grasp detection model also need to be ad-

Fig. 4. Visualization of real grasping for GC (first row) and DPG (second row) in different scenarios. The anti-collision performance of grasping throughDPG has been significantly improved compared to the original GC grasping method, and effectively reduce the damage during the robot grasping process.

Fig. 5. Visualization of FPD (first row) and FPP (second row) in different scenarios. It can be observed that regardless of the changes in the scenario,
when both human hands and ArUco markers appear simultaneously, the ArUco markers are capable of effectively inhibiting the operation of the model.
TABLE IR ESULTS OF DEPTH PRIORITIZATION
Scenarios

1

2

3

4

5

6

7

8

9

10

Overall (%)

GC-R
G-Acc
DPGC-R
DPG-Acc

5/10
4/10
2/10
10/10

3/10
6/10
4/10
8/10

5/10
5/10
3/10
6/10

4/10
5/10
2/10
8/10

3/10
7/10
1/10
9/10

4/10
5/10
2/10
8/10

4/10
6/10
3/10
6/10

3/10
6/10
3/10
7/10

3/10
7/10
1/10
9/10

2/10
7/10
3/10
7/10

36.0
58.0
24.0
78.0

TABLE IIR ESULTS OF FALSE - POSITIVE P ROTECTION
Scenarios

1

2

3

4

5

6

7

8

9

10

Overall (%)

FPD-R
FPP-Acc

8/10
9/10

9/10
8/10

7/10
7/10

8/10
9/10

8/10
9/10

10/10
8/10

10/10
8/10

8/10
10/10

9/10
7/10

7/10
8/10

84.0
83.0

dressed. Therefore, in our future research, we will pay more
attention to the design of the safe grasp detection model.
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