DYNAMO-GRASP: DYNAMics-aware Optimization for GRASP Point Detection in Suction Grippers
Authors: Boling Yang
Soofiyan Atar
Markus Grotz
Byron Boots
Joshua Smith
Venue: CoRL 2023 Poster (2023)
Keywords: Suction Grasping
Manipulation
Deep Learning
Vision

Abstract: In this research, we introduce a novel approach to the challenge of suction grasp point detection. Our method, exploiting the strengths of physics-based simulation and data-driven modeling, accounts for object dynamics during the grasping process, markedly enhancing the robot's capability to handle previously unseen objects and scenarios in real-world settings. We benchmark DYNAMO-GRASP against established approaches via comprehensive evaluations in both simulated and real-world environments. DYNAMO-GRASP delivers improved grasping performance with greater consistency in both simulated and real-world settings. Remarkably, in real-world tests with challenging scenarios, our method demonstrates a success rate improvement of up to 48\% over SOTA methods. Demonstrating a strong ability to adapt to complex and unexpected object dynamics, our method offers robust generalization to real-world challenges. The results of this research set the stage for more reliable and resilient robotic manipulation in intricate real-world situations. Experiment videos, dataset, model, and code are available at: https://sites.google.com/view/dynamo-grasp.

DYNAMO-GRASP: DYNAMics-awarr Optimization for GRASP Point Detection in Suction Grippers
Boling Yang*), Soofiyan Atar*!, Markus Grotz!, Byron Boots', Joshua R. Smith The University of Washington Authors Contributed Equally
Abstract: In this research, we introduce novel approach to the challenge of suc- tion grasp point detection. Our method, exploiting the strengths of physics-based simulation and data-driven modeling, accounts for object dynamics during the grasping process, markedly enhancing the robot's capability to handle previously unseen objects and scenarios in real-world settings. We benchmark DYNAMO- GRASP against established approaches via comprehensive evaluations both simulated and real-world environments. DYNAMO-GRASP delivers improved grasping performance with greater consistency in both simulated and real-world settings. Remarkably, in real-world tests with challenging scenarios, our method demonstrates a success rate improvement of up to 48% over SOTA methods. Demonstrating a strong ability to adapt to complex and unexpected object dynam- ics, our method offers robust generalization to real-world challenges. The results of this research set the stage for more reliable and resilient robotic manipulation in intricate real-world situations. Experiment videos, dataset, model, and code are available at: https::/sites..go/// google com/vies/dynamo- grasp."
Keywords: Suction Grasping, Manipulation, Deep Learning, Vision
Introduction
Grasp point detection is essential for successful robotic manipulation, as it requires identifying the optimal location object for robot to securely grasp and manipulate. Rapid and reliable grasp- ing capabilities for wide range of objects can benefit various applications, such as warchouse and service robots. Suction grasping is a popular grasping modality in real-world settings due to its sim- plicity and reliability when handling objects with nonporous, flat surfaces compared to parallel-jaw or multi-finger grasping. Existing methods for finding suitable grasping areas for suction grippers typically focus on maximizing suction seal quality and robustness against wrenches, taking into account the object's shape, size, and surface properties [1, 2].
Most existing methods for suction grasping assume a top-down manipulation setting, where objects are initially placed on a stable, flat surface before being grasped, and the robot attempts to grasp the object from above. This is due to the suction cup gripper requiring the robot to apply a specific amount of force to press the suction cup against the object's 's surface, which causes the cup to deform and create an air seal, resulting in a secure suction grasp. Consequently, an object being grasped needs sufficient and stable support in the direction opposite the robot's pushing. Without such support, the object may move in unfavorable direction, leading the suction cup's failure to form the air seal. However, numerous real-world scenarios require a robot to grasp objects without stable support, such as grasping from a container with side opening or from an unstable pile of objects. In these situations, the objects may exhibit significantly more complex dynamics during the manipulation process due to the displacement caused by the robot's motion and the objects' interactions with one another. State-of-the of-tte-art grasp point detection methods for suction grasping could suffer from these complex object-picking scenarios because they do not consider the objects'
Our dataset and code are open-sourced at: https::/ htaps:///ithubb cow/dynamo--oooooooooooo grwsp/dynano-graaatt
7th Conference Robot Learning (CoRL. 2023), Atlanta, USA.

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movement during the manipulation process. This limitation greatly restricts the range of scenarios in which suction grippers can be applied, preventing them from reaching their full potential in real- world manipulation tasks. Fig.l.a. illustrates a real-world manipulation task.
In this work, our goal is fully exploit the potential of suction grippers by developing grasp point detection model that not only examines quantitative metrics such as suction quality but, more importantly, considers the object dynamics during the picking process. This paper makes the fol- lowing contributions: 1. Suction Grasping by Taking Object Movement into Consideration: We describe the challenge of complex object movement during suction grasping, which no current state-of-the-art method adequately addresses. 2. An Open Source Novel Suction Grasping Sim- ulation: To address this challenge, we developed a environment using Isaac Gym[3]. This simulation environment models the influence of object dy- namics the success of suction grasps throughout the grasping process. 3. A Dataset and Learned Model: Utilizing the simulation environment, generated dataset that contains more than one million simulated grasps and trained grasp point detection model that takes into account how the movement of objects and the robot's kinematics impact the success of grasping. 4. Evaluation in a Real-world Warehouse Setting: We assessed two grasp point detection approaches alongside our model. In both simulated and real-world experiments, our method surpassed the alternatives in terms of accuracy and consistency.
suction grasping simulation
2 Related Work
Suction-based robot manipulators have gained widespread popularity in real-world applica- tions. For instance, suction grasping methods used in manufacturing [4, 5, 6]. warchous- ing [7, 8), underwater manipulation [9, 10], food and fruit manipulation [11, 12, 13. 14], etc. Another major direction where suction grasping has been applied is the exploration of end-effector modalities [15, 16, 17, 18].
a.
b.
Analytic Models. In the realm of conventional suction cup grippers, the effective analysis of grasp quality necessitates the modeling of var- ious cup properties. Given that these suction cups are typically fashioned from elastic mate- rials, such as rubber or silicone, researchers fre- quently employ apring-mass systems repre- sent their deformations [1. 2, 19]. Upon estab- object using suc- tion gripper, the suction cup is typically mod- eled as a rigid entity. The subsequent analysis involves assessing the forces imposed on the object, encompassing those along the surface normal, friction-induced tangential forces and suction-generated pulling forces [20]. Mahler et al. [1] introduced combined model in DexNet3.0, incorporating both torsional fric- tion and contact moment within a compliant model of the contact ring between the cup and the object. This amalgamated model has demonstrated its efficacy and is employed in subsequent works [2, 21]. Additionally, this work adapts the analytic models from DexNet3.0 for the purpose of data annotation.
Vg
C.
lishing secure grasp
RFD
Figure 1: a. Suction grasping for rorl-world see- narios remains challenging due to limited analy- sis of object movements. b. SOTA methods only reason for object's surface properties. Left: The quasi-static spring model. Right: Wrench basis for the suction cup. [1] Left: A warehouse picking scenario. Middle: DexNet failing the grasp due to object toppling. Right: An effective grasp point that prevents unfavorable object movements. See the project website for experiment videos.

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Learning Suction Grasps. Machine learning research in robotics has been actively exploring the se- lection of optimal grasp points to enhance suction grasping for intricate manipulation tasks [22, 23]. These tasks include novel object picking, object stewing, picking from containers, etc. Existing approaches generate training data through either human expertise [24] or simulations [1, 2. 22, 25]. DexNet3.0 [1], for instance, synthesizes training data and proposes suction grasp points that aid in forming effective suction seal and ensuring wrench resistance. Several other studies cen- ter around clustered scenarios by creating models that take RGB-D input and predict graspable points [2, 25, 24]. Jiang et al. [22] proposed a methodology that simultaneously considers grasping quality and robot reachability for bin-picking tasks. Despite these studies primarily focusing on an- alyzing surface properties or robot configuration, they largely overlook how the displacement of the object during the picking process might impact the success of the task. Addressing this particular aspect is the main focus of our work.
Visual Pushing. This project also shares relevance with the active research area of object dis- placement modeling during manipulation [26, 27]. Effective non non-prehensile manipulation strategies have been successfully applied to enhance grasping operations [28, 29, 30]. Recently, reasoning object translation via visual input has gained huge advances. Transporter and its variants [31, 32] have introduced a data-efficient learning paradigm that links visual inputs to desired robotic actions. Nonetheless, these methods are underpinned by a strong assumption of translational equivariance in visual representation, a condition that is often not met in non-table-top settings. Visual foresight methods [33, 34] have offered a model-based framework that predicts future observations based on state-action pair. However, these approaches necessitate searching through the action space given specific task, which can be time-consuming for intricate real-world problems, Other existing studies [35, 36, 37] have examined robotic manipulation from a sideways perspective. However, none of them have explicitly modeled the complex dynamics caused by the interaction between the robot and the objects.
3 Problem Statement
Our objective is to identify grasp points on target object within container filled with multiple items, using single-view depth image observation. The identified grasp points should enable robot to successfully establish a suction grasp, even when the object lacks stable support in the direction opposite to the robot's push. Consistent with previous suction grasp point detection stud- ies [1, 2, 21], a grasp point is defined by a target point [p, v]. Here, p E IR3 represents the center of the contact ring between the suction cup and the object, while 52 denotes the gripper's approach direction. The grasp labeling function is defined if the grasp successfully forms a suction grasp on the target object, and 0 if it does not. This section discusses the crucial criteria in order to form successful suction grasp.
3.1 Seal Quality and Wrench Resistance
A suction cup is capable of lifting objects owing to differential in air pressure. This differential is created across the cup's 's membrane by a vacuum generator, which pulls the object toward the cup. Ensuring tight seal between the suction cup and the target object is crucial for successful operation. For sealing evaluation, we follow the highly effective quasi-static spring-based model proposed in DexNet 3.0 [1]. As shown 1ig.1.b, this model uses a combination of three spring systems to represent suction cup deformation. A perimeter springs system is used assess the deformation between adjacent vertices, namely v; and U1+1- The cone springs system signifies the deformation of the suction cup's 's physical structure, determined by the distance between v; and Lastly, the flexion springs, which connect vertex to 0:42, are employed to resist bending along the surface of the cup.
When the suction cup forms an air seal with the object, the suction gripper should be able to resist wrenches that are caused by gravity or other disturbances. The suction ring contact model proposed in DexNet 3.0 [1] efficiently encapsulates the forces experienced by suction cup during grasp. As depicted in I.g. this model takes into account five forces. The actuated normal force (fe) and
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vacuum force V represent the gripper pressing into the object along the contact axis and air suction pulling the object, respectively. The friction forces (Jzssy) and torsional friction (r2) result from the normal force exerted between the suction cup and the object, acting as resistive forces. Lastly, Details Physics Modeling
Data Generation
Model
Encoder
Pecooe Da
Sat.
fgruxp
d.
Data Set
Output
Depth
Object Mask
Labeled Points
Figure 2: An overview of the proposed pipeline: a. We conducted system identification using 19 everyday objects of diverse shapes, weights, volumes, and materials to ascertain the function F discussed in Section 4. b. Calculation of deformation score at each simulation time step. c.&d. Gen- erating dataset with our simulation environment. e.&f. Trained DYNAMO-GRASP model outputs an affordance map highlighting optimal grasp areas.
the elastic restoring torques (Tx, Ty) result from the elastic restoring forces within the suction cup, which apply torque the object along the boundary of the contact ring.
3.2 Object Movement
Most established suction grasping techniques assume little movement of the object during the process, which facilitates the deformation of the suction cup, thereby enabling the formation of an air seal for secure grasp. However, in various practical manipulation scenarios, the target object might not have ample and steady support opposite the robot's push. This lack of support can lead to undesirable shifts in the object's position, preventing the successful creation of the air seal. The situation becomes even more complex when other objects are located near the target, due to the interactions among them. This work addresses these complexities by modeling the movement of objects during the picking process, which enhances the applicability and efficiency of suction-based grippers in real-world manipulation tasks. Assuming that object's state is denoted by its Cartesian pose and velocity in workspace, represented as = (p, 6p), the states of i objects in container at time & can be represented as {stt, .... St, }. At each time step, a robot equipped with suction gripper performs a pushing action a₂ = (fe, P. v). applying force fe to a specific location, p. on the object's surface the direction of v. The state transition model p(s++1) = T(se, ar) provides a distribution over the potential movements of the objects during the picking process.
DYNAMO-GRASP
This section proposes robot learning pipeline designed to create grasp point detection model. This model suggests suction grasp points by analyzing combined information regarding object sur- face properties and object movement during the picking process. We first implemented a new suction grasping simulation environment that accurately simulate suction cup properties and ob- jects' displacement caused by the robot's 's motion and the objects' interactions with one another. A transformer- based model trained take depth image as input and generates an affordance map over the target object's surface, indicating the likelihood of successful suction grasp if robot executes pushing action along the surface normal across various areas of the object. Please note that method primarily focuses analyzing the impact of physical interaction between robots

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and objects on the quality of suction grasp. During execution, we filter out grasp points that of- fer inadequate air seals and wrench resistance, based on DexNet's output. Fig.2 shows the system architecture.
4.1 Simulation Environment and Data Generation
In order to eliminate the need for expensive real robot data collection, we carefully designed a sim- ulation environment that accurately replicates the physical properties of the suction cup, the motion of objects caused by robot grasping, as well as the robot's kinematics during the picking process. grasping simulation environment based Isaac Gym, allowing all
We chose to implement computations to be accelerated via GPUs. While Isaac Gym lacks important features that emulate detailed suction grasping properties, our environment integrates several custom-implementted func- tional modules. It provides a pipeline that accurately simulates a puction-picking process by taking into account factors such as suction cup properties, robot kinematic constraints, collisions, control noise, and object dynamics. Additional implementation details can be found Appendix.A.1
Modeling Suction Properties. The majority of popular physics simulations for robotics merely simulate suction grasp through simplistic mechanisms. These mechanisms typically involve directly attaching the object to the robot's andeeffector or creating an attracting force between the object and the effector. However, these approaches neglect critical physical details. Specifically, to successfully register a suction grasp. the suction cup must be pushed and deformed to a sufficient extent that the rim of the suction cup attaches to the surface the object, thereby forming an air seal. Modeling the amount of force required to form an air seal is crucial for this problem. This is because when the target object lacks rigid support, exerting sufficient force directly causes the object's displacement. Understanding the magnitude of the force that the robot exerts on the target object is instrumental in recreating accurate object dynamics. To model the deformation properties of the suction cup, we first adopted the Perimeter Springs in the quasi-static spring system, as discussed in Sec 3.1. Given a grasp point p on the object's surface and the angle of incident v, this model calculates a suction deformation score for
critical
max(r1, "2,..., Tn), where r; min(1, (1, 104 - 4)/[[) Here, I; represents the original length of the perimeter spring linking vertex U; and U++1, and 1 is the length after projecting the vertices onto the object's surface. Using real-world data, we then conduct a system identification process to ascertain the function F. This function signifies how forcefully the robot needs to press the suction cup to achieve successful grasp, given a deformation score of a specific grasp point: (Saáfform) - ggrssp
Simulating Grasping Physics. (1) Kinematics: Our simulation accepts robot's 's model as input and controls the robot using an end-effector controller to attempt various suction grasps. This approach enables the simulation to demonstrate how the robot's s form factor and kinematic properties impact its grasp. For instance, some grasp points might be physically unattainable for the robot due to its manipulability and reachability constraints collisions. (2) Generating Scenarios: Our experiment primarily focuses warehouse lateral picking scenario. During data generation process, the simulation randomly selects one to three objects from object set and spawns them into the same container with random positions and orientations. We also implement domain randomization for observation noise, objects' weights, and controller parameters, ensuring the dataset reflects range of diverse physical properties and robot behaviors. One of the objects in the container is randomly assigned as the target object to be picked. (3) Sampling Grasp Points: Given picking scenario, we sample two sets of candidate grasp points from the visible surface of the target object. The first set is derived from uniform sampling across the entire surface, ensuring that the robot explores diverse picking strategies. The second set contains the grasp points with the highest score returned by DexNet via the Cross- cross-Entropy Method (CEM) sampling strategy, ensuring the robot explores areas that DexNet deems preferable.
Labeling Data. After sampling the candidate grasp points, our simulated robot 'physically' exe- cutes each candidate p by performing sequence of pushing actions A 17 where each action a, (fe. p. v). Here, is determined by the surface normal p. The robot exerts a stant force fo = fo if the target object is unstable and moves in response to the gripper's push.
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Once the object finds position with adequate support against the push, fi gradually increases until the suction cup deforms enough to form an air seal or until the object starts moving again. The simulation of the suction cup's deformation and the precise estimation of suction grasp registration involves a continuous calculation of form and Sgrasp at each timestep. This process considers the suction cup's current position relative the target object, as shown in Fig.2.b. force sensor the end-effector continuously monitors ft, and grasp is deemed successful if ft ggrasp- Any failure to meet this condition, such collisions inaccuracies in end-effector positioning due to manipulability reachability issues, results in the grasp point being marked as unsuccessful. For successful grasps, we incorporated penalization term Pmove into the label to penalize unnecessary object movements. Further details are discussed in Appendix.A.1.
4.2 Model Training
We employ the suction grasping simulation, described above, to generate dataset. This dataset represents a warehouse scenario where a robot equipped with suction gripper is tasked with extract- ing a target object from a small container filled with multiple unorganized items. The experimental setting is detailed in Sec.5. This dataset was used to train a model for grasp point selection. The model takes a single-view point cloud of the container's interior, segmentation mask identifying each object within the container and its boundaries inputs. then outputs affordance map representing the estimated probability of successful grasps at all potential grasp points the target object. The largest value in the affordance map indicates the optimal grasp point, (p", v²), for the given scenario. As shown in Fig.2.e, our model employs auto-encoder architecture, integrating transformer encoder and a deconvolutional decoder. As previously mentioned, our data generation process is designed to capture the inherent variabilities complex real-world robotic suction grasp- ing tasks. These include variations stemming from the physical properties of different objects, robot constraints, and stochasticity in the controller, among others. Empirically. we discovered that the following loss function effectively mitigate the adverse effects of the high aleatoric uncertainty in our dataset during training: Lym.x N Ei= (yi 2) ). Vys E Yman. Here, Ymaz represents a subset of samples containing the highest-scored scored grasp points on an object. More details are discussed in Appendix.A.2.
Experiment
Our experiment focuses on robotic suction grasping for industrial ware- house shelves [38]. Fig.1 depicts the robot setup and the industrial shelv- ing unit which is packed with ob- jects. The opening of these shely- ing units is located on the side, which makes suction grasping significantly more challenging compared to top- down manipulation scenarios, as robot's movements can trigger se- ries of object displacements, leading to objects being shifted even top- pled over. Consequently, this sce- nario serves as an excellent evalua- tion environment for our work. Our system setup is as follows. Through- out our evaluation, we employed Universal Robots UR16e robot equipped with Robotiq EPick suction gripper and an Intel Re- alsense L515 camera mounted on its wrist. A large variety of objects with different shapes, dimen- sions, and physical properties were used in experiment, details can be found in Appendix.A.I.
b.
C. d.
Figure 3: Left: The simulation environment for data gen- eration and experiments. The simulated objects with differ- ent weights, sizes, and shapes are displayed on the left side of the robot. Right: In Section 5.1, challenging test cases are presented where only DYNAMO-GRAS ful in grasping the target object. The orange, blue, and yel- low points indicate the grasp points proposed by DYNAMO- GRASP, DexNet, and the Centroid method, respectively.
was success-

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Dyn(Full)
Dyn w/ MSR
Dyn w/o PEN
Dex
Cen
Total Success Rate
88.05%
86.75%
82.93%
81.12%
78.78 78.78%
Success Std
0.30
0.32
0.36
0.36
0.40
Table 1: The first row of the table displays the grasping success rate for each method, calculated from all 1300 picks. The second row provides the standard deviation of the success rate for each method across various scenarios. The first three columns of the table present an ablation comparison for our DYNAMO-GRASP (DYN) method, while Dex and Cen represent the DexNet and Centroid methods, respectively.
In our experiment, we focus on evaluating three methods: our method DYNAMO-GRASP (Dyn), 2. DexNet3.0 (Dex), and 3. the Centroid method (Cen). DexNet3.0 is SOTA suction-picking tech- nique, serving strong baseline. Meanwhile, the Centroid method, straightforward approach involving suctioning on the object's centroid, has proven effective in similar tasks at the Amazon Robotics Challenge (3733)
5.1 Large-scale, Diverse Scenario Assessment, and Ablation Test
To comprehensively assess the performance and robustness of various methods for the suction grasp- ing challenge, we generated 260 diverse picking scenarios. We use the same simulation environment as we used to generate our training dataset. Each of the three methods was tested with five suction grasps per scenario in simulation, resulting in 1300 simulated suction grasps for each method's eval- uation. The scenarios were generated by sampling from distribution that incorporates even greater randomness in object orientation than the dataset used for model training. These scenarios incorpo- rate wide range of object configurations, leading to potentially complex object movements during picking.
Comparing the first, fourth, and fifth columns of 1able. it is evident that our method exhibits a marked improvement over both DexNet and the Centroid method terms of overall success rate and consistent performance across various scenarios. Our method achieved the highest success rate of 88.05% % and exhibited the least variance in success across different scenarios. The first, second, and third columns of Table.1 presents ablation test that illustrates the contributions of various components in our learning pipeline to the effective training of our model. Dyn(Full) is our final model, Dyn w/ MSR represents a model trained with standard MSR loss instead of the Lymax described in Sec.4.2, and Dyn w/o PEN further remove the use of penalization term Pmove in the labeling process.
5.2 Real-world Evaluation
To assess real-world efficacy, we executed 375 real-world suction grasps to evaluate the various methods. In this experiment, we curated three sets scenarios: the Common set, Challenging set, and Adversarial set, each embodying distinct level or type of challenge for suction grasping. The statistic of all experimental trials and their comparison to the simulated trials detailed in Table.2, 3, and 4 in Appendix.A.3.I.
The Common Set: In this experiment, we sampled ten scenarios from the 260 randomly generated ones as detailed in 1. We then recreated these scenarios in the real world using objects with dimensions similar those in the simulations. Each method was used to perform five grasps on each of these scenarios. This evaluation set captures the typical challenges of most picking tasks in this specific warehouse environment. As shown in Fig.4, total success rate of 94%, averaging 4.7 successful grasps out of five attempts and standard deviation of 0.67. In contrast, both DexNet and the Centroid method average 4.2 successful grasps out of five attempts. Their higher standard deviations, 0.92 and 1.03 respectively, point to less consistent performance.
model demonstrates an advantage with
The Challenging Set and Adversarial Set:
We are particularly interested in the more challenging cases. Consequently, devised two sets of scenarios in the real world further test the capa- bilities of the three methods. The Challenging Set comprises five scenarios from the 260 scenarios
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DYNAMO
64%
54%
DexNet
60% 75%
Total Success Rate
701
II Centroid
60%
50%
40%
50%
28%
26%
20%
10%
0%
Common Set
Challenging Set
Adversarial Set
Figure 4: Comparison of the total success rates of different methods underscores their real-world performance on the three evaluation sets described Sec.5.2. The total success rate computed by dividing the number of successful grasps by the total number of attempts within an evaluation set.
described in Sec.5.1. These scenarios exhibit the lowest combined success rate for the three methods in simulation, representing the most challenging situations our simulation generated without human bias. In contrast, the Adversarial Ser comprises five scenarios designed by human operator, specif- ically tailored to challenge these grippers. The objects featured this set are everyday items that were not included during the training phase. As depicted in Fig.4 and 3,b in Appendix.A.3.1, DYNAMO-GRASP markedly outperforms the two baseline methods in both total success rate and performance consistency in more challenging scenarios. On the challenging set, our method achieved success rate of 60%, whereas, the adversarial DexNet and the Centroid method's success rates are 24% % and 36% for the challenging set, with both achieving 28% on the adversarial set. Furthermore, DYNAMO-GRASS consistently executed more than four successful grasps out of five attempts in over half of the scenarios in both sets. Meanwhile, the other two methods faltered, rarely managing even three successful grasps in any scenario within these evaluation sets.
it reached 76%. In stark contrast,
Qualitative Analysis. The Fig.7 in Appendix.A.3.I depicts the grasp points chosen by various methods and indicates the success of each attempt during the adversarial evaluation. The figure offers insights into the areas chosen by each method for grasping and sheds light on which areas are more likely to lead to successful grasps. For example, in the first scenario, a tall bottle is partially propped up by a box the back. The test checks the grasp method's awareness of potential object toppling. DYNAMO-GRASP chose the bottle's lower part, ensuring the box supported the pick. Some grasp points chosen by the other two methods were higher up on the bottle leading toppling movements. Similarly, in scenarios two, four, and five, DYNAMO-GRASP tends to select grasp points from regions that are overlooked by the other methods, resulting in more successful grasps in these scenarios.
Conclusion and Limitation
This paper discusses the challenge of complex object movement during suction grasping, which no current state-of-the-art method adequately addresses. We introduced DYNAMO-GRASP, dynamic-aware grasp point detection method that selects grasp points by factoring in the impact of object movement on the success suction grasping. DYNAMO-GRASP delivers improved grasping performance with greater consistency in both simulated and real-world settings. Notably, real-world experiments involving challenging scenarios, our method exhibits an improvement of up to 48% in success rate compared to alternative methods. Limitations and future work: Firstly, the dataset used in our simulation environment primarily includes objects with relatively simple ge- ometric shapes. This aspect could limit the efficacy of our method when dealing with objects of uncommon or complex shapes. Similarly, our real-world experiments primarily involved simple ge- ometric objects, such as boxes and bottles. In future research, there's 's potential to develop effective heuristics that combine information from both DYNAMO-GRASP and DexNet. While our method emphasizes modeling object movement, DexNet primarily targets suction quality based on object surface geometry. Integrating the strengths of both methods could lead to enhanced performance in specific applications.
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Acknowledgments
This research is funded by the UW Amazon Science Hub part of the project titled, "Robotic Manipulation in Densely Packed Containers." We would like to thank Dr. Michael Wolf from Ama- zon for valuable discussions. We would like to thank Yi Li for many constructive inputs to the machine learning aspect of this paper. We thank all members of the Amazon manipulation project who generously lent us their computing resources and robot time.
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12

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A
Appendix
A.1 Simulation Details
A simulation that accurately replicates the targeted robotic tasks can significantly enhance the effi ciency of various machine learning algorithms in learning these tasks[40, 41, 42, 43J. Most physi- cal simulators, such as Mujoco[44], PyBullet[45], and IsaacGymm31, which excel at simulating the physical properties of object motion, lack the functionality to simulate the characteristics of suction cups during suction grasping. Our development efforts focus on utilizing the sensing and physical information in IsaacGym to create more realistic suction-picking properties.
System Identification: Our system identification process aims to accurately model the force re- quired by the robot to deform the suction cup. This ensures the rim of the cup adheres to the object's surface, forming an air seal. We chose 18 everyday objects with varied surface geometric charac- teristics, aiming to cover a broad spectrum of deformation scores. For each object, we executed ten suction grasps using our UR16 robot. To minimize measurement noise, the objects were held firmly to limit movement during the grasping process. The force required for the suction gripper to achieve suction seal was detected by sudden decrease in suction airflow and the force torque sensor lo- cated on the robot's wrist. We observed that the characteristics of our suction cup differ significantly between nearly flat object surfaces and those that are more curved or intricate. Consequently, we chose to represent the function F(Sde form) - fgrasp using a hybrid linear function:
7.66 0.06 * Sde
if Sanform S 22.2 0.18 * farform. otherwise
F(Sde form)
Typically, the size and firmness of a suction cup influence its working range for objects of varying sizes and weights. However, this doesn't profoundly alter the nature of this grasping problem. For instance, when using a small suction cup to manipulate lighter, smaller objects, these objects typi- cally have less friction with the container and reduced inertia, making them more prone to toppling. However, though anticipate certain degree of generalization unseen suction cups, recommend carrying out the system identification process to achieve optimal performance.
Systee Montiiiatioon TE calondal skyets
Sycter Montiicatoon cylidincal ekeuts
Cubeiial cylindiical oijects Below Cyliditicll Objects
Figure 5: Force exerted on an object as a function of the suction deformation score. Solid lines represent system identification fits for cylindrical (blue-colored line) and cuboidal (violet-colored line) objects. The dotted line demarcates the distribution of data points between the two object types.
Domain Randomization:
We performed domain randomization to vary object weights, where each of the ten chosen objects had their original weights varied by -5g, -10 g. 5g, and 10g,
13

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leading to five weight versions per object. These objects were cylindrical or cuboidal with varying dimensions, inertia, and weights. The cylindrical ones had radii from 26 mm to 46.3 mm, heights between 95 mm and 155 mm, and weights ranging from 118 g to 602g g. In contrast, the cuboidal objects had lengths from 112 mm to 165 num, breadths between 55 mun and 102 min, and widths from 55 mm 95 mm. Notably, with every weight change, the inertia properties were appropriately modified. When placing objects in the simulator, their orientations all three axes were uniformly picked from 180 to 180 Although their initial placements followed predefined bin coordinates, potential collisions might displace some objects. As preventive measure, we ensured that each object remained within the bin limits and verified the stability of each object setup by spawning it thrice and monitoring its movement at each simulation step for minimal displacement until the suction gripper gets contact with the target object. Lastly, to closely mimic our physical robot setup with the Intel RealSense L515 camera, we added Gaussian noise (mean: mm, standard deviation: 0.9 mm) the depth images.
Labeling: For the label for each configuration, each grasp point score serves as an indicator of grasp success. A grasp point that fails to achieve a secure suction grip is assigned a definitive zero score. Additionally, the label is designated as a 'failure' if the robotic arm does not align and picks the object at the computed angle of incidence derived from the surface normals, ensuring the grasp adheres the pre-calculated optimal orientation. Another critical constraint is that the arm must avoid unintended contact with any other object before establishing contact with the target, as such collisions can compromise the grasp's integrity and lead to potential inaccuracies or damage. On the other hand, successful grasp points are scored using the equation = 1 Pmove. where,
Pmooe = max(0, minnoojjmmocemeeet mooementt 0.3))
obj movement
(I]tranc+ - trans,|| + (1 - |quat quat,))
Piooe is a penalization term that discourages unnecessary movement of the target object. obj movement calculates the total movement of the target object during the picking process. The picking horizon T is discretized by a fixed interval, and t represents a time step within T. tran and quat represent the translation and orientation of the target object at given time step, respectively.
dataset to enhance our model's resilience against variances in wallworld scenarios. We added Gaussian noise to the point cloud data and flipped the input data along with their corresponding labels, strengthening the model's ability to recognize various object orientations and thereby improving its generalization capabilities. These augmentation strategies significantly expanded the diversity of our training dataset, ensuring the model's proficiency in managing diverse input perturbations. The complete dataset, including labels and augmented inputs, consisted of around 12000 configurations, including augmentations, enhancing the dataset's diversity and depth, which occupy approximately 10 GB of storage space.
Dataset: We implemented specific data augmentation techniques
A.2 Learning Details
Our model employs a variation of the Vision Transformer (ViT), adopting the architecture from Beyer et al.[4 We chose ViT because it represents a state-of-the-an architecture widely used vision classification tasks. Utilizing this architecture demonstrates that a standard network, when trained with our dataset, effectively addresses the challenge of suction grasping in complex object clusters. This is achieved without the need for custom modifications to the model architecture.
Model Architecture:
Loss Fuction: We initially experimented with both the standard MSE loss and Cross-entropy loss but observed only mediocre performance from the model. As highlighted in Section 4, the domain randomization process introduced significant stochasticity to our dataset. Empirically, we found the presented loss function to be more effective in this specific context. The use of Ymoz is a simple technique designed to mitigate the adverse effects of high alcatoric uncertainty present in the training data. It updates the model by only taking into account the grasp points that the model deems to have
14

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Training Accuracy
Training
0.2
0.3
Epects
spo212-
422
103
330
506
Accuracy Epochs
Epochs
9.35
0.1
2.4
Figure 6: Training and validation metrics over epochs: The top row displays the metrics related to training. with the left graph showing the training accuracy (calculated using all grasp points) and the right graph presenting the training loss (determined with 15 highest-scored grasp points). The bottom row focuses validation metrics, with the left graph illustrating the validation accuracy (using all grasp points) and the right graph depicting the validation loss (using the 15 highest-scored grasp points)
high confidence of success. This approach is aimed at penalizing false positive predictions made with high confidence or encouraging true positive predictions made with high confidence while disregarding low confidence labels, which usually arise due to data noise. We discovered that this loss function led to improved prediction accuracy and produced a smoother affordance map.
We trained our VIT model using the Adam optimizer with learning rate of 5e-5 and batch size of 128 images. The model converged in about 500 epochs, and the training was conducted on an NVIDIA RTX 3090. Our ViT model consists of eight heads, each with dimension of 64. Consequently, we set Q, K, and V to 128, 257, and 1536, respectively. The model accepts x 256 x 256 tensor as input. The first, second, and third channels represent the x, y, and values of the cropped point cloud observation for the container. The fourth channel provides segmentation mask that localizes the target object. The model produces 256 256 affordance ranging from 1. A higher score indicates more
Hyperparameters:
map. Each pixel in this map provides favorable grasp point for achieving a successful suction grasp.
Segmentation Mask: Within the Isaac GYM simulator, we adhere to ground truth segmenta- tion masks. For real-robot experiments, we used a specialized method, called "STOW" [47], that combines VITA [48] and the Mask2Former [49], which is tailored for joint unseen object instance segmentation and tracking. The method uses transformer-based architectures and dynamic tracking anchors to handle real-world visuals characterized by dense clustering and substantial intra-frame object displacements.
Grasp Point Selection: After getting the affordance map, we first identify the pixels that represent the target object in the map using the segmentation mask. Subsequently, we use the DBSCAN algorithm [50] to cluster regions displaying high-affinity scores exceeding 0.9. During this clustering phase, each cluster encompasses minimum of five pixels. The final grasp point is defined by the centroid of the cluster with the highest average affinity score.
15

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DYN
SUcOnSS
Faluure
ME
Dex
Fallure
MODGA
Cen
Falkra
MIEGGA
Figure 7: Real-world adversarial evaluation with five grasp points for each configuration: DYNAMO GRASP (our method). DexNet, and Centroid. The color-coded points represent the suggested grasp points success and failure from various algorithms. The successfully identified grasp points are marked by the color along the label "success" and "failure".
A.3 Experiment Details:
In the real-world environment setup, distinct from the simulator approach, objects were first stowed into designated bin. Following this, segmentation algorithm employed to generate mask delineating each object. The user then selects the target object based on its unique value in the grayscale mask image, referred to as the 'target object id'. With the object identified, the next phase involves running the inference of a user-provided algorithm to determine the optimal strategy for picking the selected object. The entire operation is orchestrated through a state machine, ensuring seamless transition between stages. Each state connected sequentially. In evaluating success and failure across various methods, grasp point is deemed unsuccessful motion planning fails consecutively on two occasions. Additionally, if the system does not create a suction with the object, it is also considered failure. A successful grasp is solely determined by the creation of good suction with the target object.
A.3.1 Extra Experimental Result:
The Sim2Real Gap: Our DYNAMO-GRASP model was exclusively trained using simulated data. In most of our experiments, this model exhibited outstanding real-world performance without requir- ing tuming using real-world data. This indicates the model's strong ability to generalize in real-world conditions, showeasing a minimal sim2real gap. To delve deeper into our pipeline's constraints, we pinpointed situations where simulation deemed the target object "impossible" to pick up. In these instances, all three picking techniques registered a 0% success rate in simulation. Importantly, these situations are infrequent, accounting for just around 3% of the 260 simulated test scenarios. We then mirrored these situations in an actual warehouse environment and ran real robot experiment as delineated in Sec.5.2. Despite the struggles faced by all three methods to secure high success rates (DYN: 16%, Dex: 8%, Cen: 40%), the real-world challenges weren't 't as formidable as projected by the simulation. However, this did highlight sim2real gap in these rare scenarios. Our observa- tions also revealed that, in cases where our simulation wasn't entirely accurate, the centroid method surpassed the performance of learning- based approaches. This observation emphasizes the value of refining learning-basee based models using actual world data.
16

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Common Set
Real world experiments
sim experiments
DYN
Dex
Cen
DYN
Dex
Cen
Scenario
3
3
5
5
Scenario 2
5
5
5
5
Scenario 3
5
5
5
5
Scenario 4
5
5
5
5
5
5
Scenario 5
5
5
5
Scenario 6
5
5
5
5
5
5
Scenario 7
5
Scenario 8
5
4
0
5
Scenario 9
5
2
5
0
Scenario 10
5
5
4
5
5
5
Avg. Success Grasps
4.7
4.2
4.2
4.5
4.5
Std. Dev.
0.675
0.919
1.033
1.581
1.581
Total Success Rate
94%
84%
84%
90%
90%
100%
Table 2: Comparative evaluation of grasp success rates in common scenarios for three methodolo- gies: DYNAMO-GRAN (DYN). DexNet (Dex), and Centroid (Cen). The table enumerates the average success rates, standard deviations, and total success rates for each method.
Challenging Set
Real world experiments
sim experiments
DYN
Dex
Cen
DYN
Dex
Cen
Scenario
4
I
2
5
0
Scenario 2
2
2
3
0
3
Scenario 3
4
1
0
5
0
Scenario 4
0
1
3
0
0
Scenario 5
5
1
1
5
0
Avg. Success Grasps
3
1.2
1.8
3
0.6
Std. Dev.
2
0.447
1.304
2.739
1.342
1.414
Total Success Rate
60%
24%
36%
60%
12%
20%
Table 3: Comparative evaluation of grasp success rates in challenging scenarios for three method- ologies: DYNAMO-GRASP (DYN), DexNet (Dex), and Centroid (Cen). The table enumerates the average success rates, standard deviations, and total success rates for each method.
Adversarial Set
Real world experiments
DYN
Dex
Cen
Scenario
5
2
Scenario 2
3
0
0
Scenario
Scenario 4
5
3
I
Scenario 5
5
3
Avg. Success Grasps
3.8
1.4
1.4
Std. Dev.
1.789
1.517
1.14
Total Success Rate
76%
28%
28%
Table 4: Comparative evaluation of grasp success rates in adversarial scenarios for three method- ologies: DYNAMO-GRASS (DYN), DexNet (Dex), and Centroid (Cen). The table enumerates the average success rates, standard deviations, and total success rates for each method.
17


Reviews:
[{"review_id": "zyxKrhk-GC", "reviewer": "Reviewer_TQDd", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "This work presents DYNAMO-GRASP, a dynamic-aware grasp point detection method, which accounts for the object dynamics during the grasp prediction process. The method is evaluated on object-in-shelf side-grasps scenarios, both in simulation as well as on a real robot, and its performance is compared to two baselines: a centroid heuristic and DexNet. The main contributions of the paper are 1) a new synthetic data collection pipeline, and 2) an experimental evaluation of the proposed method.", "summary_of_recommendation": "I believe that this work is addressing an important research problem and it has all the potential for a good publication. Before being able to recommend this paper, the manuscript needs to be updated with the clarifications requested above.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Excellent: Enjoyable to read.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "1. Where does the network architecture come from? If it is adopted from previous work, this must be cited, together with the reasoning behind this decision. If it is a \"novel\" architecture, many more details are needed, explaining all the trade-offs and design choices that came with it.\n2. Similarly, additional details are needed regarding the loss function: where does it come from, what other options were considered, why is Y_max important, and what effect does it have?\n3. Additional information about the training data used: how many grasps, how many labeled points, how many images, how many GB is the total dataset, etc.\n4. Additional information about the training process: how long (time and steps), what hardware was used, what hyperparameters were chosen, etc.\n5. The pipeline requires segmentation masks for each object. How were these generated? Does the simulated evaluation use ground truth masks? What about the real robot experiments?\n6. 50 scenarios in simulation and 5 in the real world are not so many. Increasing the number of runs would improve the statistical significance of the experiment results", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "I find this work very timely and important for the robotic community. As explained in the introduction, state-of-the-art grasp point detection methods for suction grasping are not suitable for complex object-picking scenarios, such as object-in-shelf side-grasps, because they do not consider the movement of the object during the manipulation process. I believe that this work represents a step forward in exploring this direction. Nevertheless, I still have open questions that I think need to be addressed before considering this submission ready for publication, please see the next section.", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed."}, "extra_text": {}}, {"review_id": "tHkMsllYKc_", "reviewer": "Reviewer_gTny", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "This paper proposes DYNAMO-GRASP, a neural network that proposes suction grasps points on objects without support in the grasp approach direction, such as objects on shelves. The actual neural network is a transformer that inputs a depth image and outputs a pixel-wise affordance map that tells how likely a suction grasp is at that specific pixel. A novel, more realistic suction cup simulation setup is introduced to gather training data. The experimental evaluation on grasping objects from shelves demonstrates that DYNAMO-GRASP achieves a much higher grasp success rate than DexNet 3.0, the current state-of-the-art data-driven suction cup baseline.  \n", "summary_of_recommendation": "The proposed DYNAMO-GRASP is a novel neural network that predicts dense pixel-wise suction grasps on objects that do not have any support from the environment in the grasp approach direction, such as objects on shelves. The results are good (better grasp success rates than the baseline), but the methodological contributions with respect to DexNet 3.0 are limited. Therefore, this reviewer recommends a weak reject rating. To improve the paper, the authors should do a more extensive experimental evaluation that includes more real-world grasps and the performance of DexNet 3.0 retrained on the new dataset.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Additional details required", "questions_for_rebuttal": "1. It seems that the statement on lines 166-168 contradicts what is written on lines 168-170. As I understood, your model outputs the grasps with the highest likelihood of succeeding (having a high grasp quality) rather than the grasp that displaces the object the least.\n2. What does $f_{grasp}$ stand for on line 201?\n3. On line 196, is $l_i^{'}$ the geodesic distance between the vertices on the object or the Euclidean distance? If it is the Euclidean distance, can $l_i^{'}$ ever be larger than $l_i$? If not, you could change the order of $l_i^{'}$ and $l_i$ in $r_i$ and eliminate the absolute value.\n4. Could you also do the DYNAMO-DexNet experiment in Section 5.1? It should not take long to complete as it is in simulation.\n5. In Figures 3 b and d, the centroid method should work better than or at least as well as DYNAMO-GRASP. Why did DYNAMO-GRASP work in those cases but not the centroid method? \n6. Is the simulation environment completely deterministic? If not, please repeat the experiments in Section 5.1 over a few (maybe 5) different seeds.\n7. In the real-world experiment, only cylinder-type objects are picked. Such objects should be favorable for DYNAMO-Grasp as the suction cup gripper deforms upon contact. For a fair comparison, also do some real-world experiments with box-type objects.\n8. As far as I understood, DexNet 3.0 is not retrained on your dataset. If that is the case, is it fair to compare DYNAMO-GRASP to DexNet 3.0 because the dataset DexNet 3.0 is trained on might not contain that many cylinder-type objects, which the proposed dataset is full of? Maybe it is necessary to compare the DexNet 3.0 dataset and the proposed dataset and check if the objects have similar shapes. If there is a vast discrepancy, DexNet 3.0 should be retrained on an object set with a larger proportion of cylinder-type objects.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper tackles a relevant and challenging problem in robotic manipulation and grasping: how to generate stable suction grasps on objects without support in the grasp approach direction. I agree with the authors that previous methods have primarily studied grasping from the top-down direction, which is a much more forgiving setting than grasping from the side.\n\nWhat I learned from the paper is that approaching the object from a direction that displaces it as little as possible is probably a good idea when grasping from the side. Another thing I take with me is that the community should look more into grasping objects in confined spaces that require non-top-down grasp directions.\n\nOriginality:\n\nThe paper addresses the well-known problem of generating suction cup grasps. However, it is original because it focuses on confined spaces, a setup that has yet to be vastly researched. The simulation approach is also novel.\n\nQuality\n\nThe quality of the experiments could be better. For instance, I think the experimental evaluation of DexNet 3.0 is unfair, as the dataset used for training that network is on top-down grasps in non-confined spaces. On the same line, the dataset used to train DYNAMO-GRASP only contains side grasps which are also the setting for the experiments, while DexNet 3.0 is not trained on such grasps. There also needs to be a discussion about how the experimental evaluation might be affected by the stiffness and sizes of the suction cup gripper.\n\nClarity\n\nThe paper is well-written and structured. There are a few typos that I point out below. \n\n- Section 5.1 has a few contractions (we've, it's, etc.) Please rewrite these into the non-contraction form.\n- On line 195, you refer to $\\mathbf{v}$ as the angle of incident, but $\\mathbf{v}$ is actually a vector representing the approach direction.\n- On line 125, the sentence ends with a colon, indicating that what follows is a list. However, I do not see such a list. Please take a look at that sentence.  \n\nSignificance\n\nIn terms of significance, the paper highlights the difficulty of grasping objects sideways from a confined space with suction cup grippers. The proposed method seems to be better at this problem than other state-of-the-art. However, as mentioned earlier, a more thorough experimental evaluation should be conducted to determine if that is the case.  \n\nRelevance\n\nBased on the paper's content, it does not address a robotic learning problem but rather a robotic grasping problem. For instance, the proposed neural network is similar very similar to previous work on suction grasping [1, 2, 3]. \n\nLimitations\n\nThe limitation section highlights limitations regarding the relatively homogeneous dataset, which is excellent. However, it should also highlight the low number of real-world experiments and that the baseline was not trained on the same dataset as their method.\n\nStrengths:\n\n- The work tackles a challenging and relevant problem in suction cup grasping.\n-  A novel, more realistic approach to simulating suction cup grasps.\n- The use of the centroid grasping baseline is an excellent complement and demonstrates how good a simple but non-learning-based method can do.\n\nWeaknesses:\n- The experimental evaluation of DexNet 3.0 is unfair, as the dataset used for training that network is on top-down grasps in non-confined spaces.\n- The dataset used to train DYNAMO-GRASP only contains side grasps which are also the setting for the experiments, while DexNet 3.0 is not trained on such grasps\n- There is no discussion on how the experimental evaluation might be affected by the stiffness and sizes of the suction cup gripper. The gripper used in this paper is considerably larger than the gripper used in the original DexNet 3.0 paper. From intuition, I think a smaller gripper needs less force to create a tight seal, which might be why DexNet 3.0 proposes grasps higher up on the objects.\n- The network architecture needs to be better described. I would not be able to re-implement it based on the information in the paper.\n\n[1] Cao, Hanwen, et al. \"Suctionnet-1billion: A large-scale benchmark for suction grasping.\" _IEEE Robotics and Automation Letters_ 6.4 (2021): 8718-8725.\n[2] Wong, Alexander, et al. \"Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge.\" _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_. 2023.\n[3] Zeng, Andy, et al. \"Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching.\" _The International Journal of Robotics Research_ 41.7 (2022): 690-705.", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed."}, "extra_text": {}}, {"review_id": "rkODmcwwTV", "reviewer": "Reviewer_GYg8", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "This paper focuses on suction grasp point detection. It addresses the task of determining the center of the contact ring (p) and the gripper's approach direction (v) based on a single-view depth image observation. The main contributions of the paper include: 1) Suction grasping simulation, 2) A robot learning framework for suction grasping that considers seal quality, wrench resistance, and object movement during the picking process, and 3) Real-world evaluation. To elaborate further, the grasping simulation involves the use of an analytical model for the suction cup, employing the Perimeter Springs system within the quasi-static spring system in Isaac Gym. During the inference process, the system queries grasp point candidates and outputs affordances, highlighting the optimal grasping points.", "summary_of_recommendation": "Suction-based robot manipulations have been studied extensively, as mentioned in the related work section of this paper. However, certain challenges persist, particularly concerning the deformable suction cup's nature, which demands vacuum sealing and pushing motion for picking, leading to the object moving while being grasped. This paper effectively addresses these challenges through sim2real techniques and demonstrates impressive real-world results. Nevertheless, there is room for improvement in terms of writing clarity.\n\nTherefore, I recommend a weak acceptance of this paper.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Fair: Major revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "There are two folds:\n1. Clarification: Can you explain how the state transition model affects the pushing action a_t? \n\n2. Writing: Is it possible to address the review-writing section?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper discusses several interesting aspects, including the simulation environment with the analytical model of the suction cup, the grasping points sampling for training (DexNet + uniform sampling), which awards the robot kinematics, and the diverse simulated environment with domain randomization. Additionally, the proposed learning cost enables the learning of a continuous affordance map from sparse grasping score samples in the training data.\n\nHowever, the paper requires some improvements in its writing, as it lacks certain essential details and is not always easy to follow. Here are some specific points to address:\n\n- Could you provide information about the input-output dimension?\n- How did you define Q, K, and V for the transformer encoder structure?\n- It would be helpful if you could elaborate more on how to obtain the optimal grasp point (p, v) (line 238) from the affordance map. For example, is the largest value in the affordance map transformed into the world coordinate as p? How do you obtain v?\n- You mentioned conducting a system identification process to ascertain the function F. Could you explain your 'system identification process' in more detail?\n- What criteria define 'challenging cases' (line 298)?\n- The phrase 'based on the following crucial criteria: (line 125)' is not a complete sentence and requires revision.\n- In line 202 'robot's model', are you referring to a robot model? If so, please make it clear.\n- The section titles '4.1 Simulation (line 174)' and '4.2 Robot Learning (line 230)' are rather vague and general. Consider improving them to something like '4.1 Simulation Environment and Data Collection' to enhance clarity.\n- Could you explain what \\hat{y}_t (line 245) represents?\n", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning."}, "extra_text": {}}, {"review_id": "PrU7XZiKlQ", "reviewer": "Reviewer_dsfY", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "The paper proposes DYNAMO-GRASP, an approach to generating suction gripper grasp points for objects without stable support, in particular in the lateral picking scenario. First, the authors implemented a simulation setup in Isaac Gym for suction gripper grasping. The simulation uses real world data to predict the forward force required to achieve a suction grasp. If the robot achieves a forward force above this value, the simulation deems it as a successful grasp. Using this method, the authors produced a dataset and trained an autoencoder that outputs an affordance map representing the estimated grasp success probability on each grasp point, the largest of which indicates the optimal grasp point. The authors evaluated their method on 50 simulated scenarios and 5 real-world scenarios. The proposed method succeeded in 98% of the simulated scenarios and 4 out of 5 real-world scenarios, which outperformed baseline methods DexNet 3.0 and suctioning on the object’s centroid.", "summary_of_recommendation": "Overall, the paper seems like a work in progress. It can be significantly improved with more experiments and a revised presentation. However, I am skeptical that these can be completed within the rebuttal timeframe. I do not recommend this submission for CoRL 2023.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "1.\tThe choice of objects is not elaborated anywhere. Are the objects seen during training? \n\n2.\tHow is f_t decided in Section 4.1 “Labeling Data”? Doesn’t the choice of f_t affect whether the grasp succeeds?\n\n3.\tWhat is the method used to fit the function F in section 4.1 “Modeling Suction Properties”? What structure and order does F assume?\n\n4.\tMore visualization should be provided for the simulation scenarios. It is difficult to judge the relevance of the simulation evaluations as is.\n\n5.\tThere is no justification on how well the data labeling scheme and suction cup simulation scheme reflect real world grasp success. The simulated scenarios should be reproduced in the real world.\n\n6.\tWhy does the miniscule grasp point difference in Figure 3b & 3d result in different outcomes? I suspect it is an artifact of the simulation evaluation metric. This should be clarified, preferably with real world evaluation.\n\n7.\tThe remark in Sec 5.2 “Consistency Test” “our method consistently suggested grasp points within the same local region” is unclear to me. How does the size of this “local region” compare with the 2cm difference in Figure 3b & 3d?\n\n8.\tThe real-world evaluation is insufficient. More scenarios and repetitions are necessary.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "While the problem tacked by this work is interesting and well-grounded in real world robotic applications, the execution of this paper needs improvement. Please refer to “Questions For Rebuttal” for more specific remarks.\n\nThe most significant weakness of the paper is the lack of evaluation. The most extensive evaluation presented is in simulation, which is solely based on a metric that the proposed method is designed around. Given the presumed difficulty in simulating a deformable suction cup, the simulated scenarios should be reproduced in the real world. The real-world experiments are very limited and could arguably be cherry picked. All scenarios were designed by the authors, and only scenario 4 has more than one execution.\n\nWithout empirical support, the merits of this paper are limited. From what I can tell, most of the novelty of this paper lies at the system level. Each of the components in the method is adapted from literature. The deformation model for the suction cup is adapted from DexNet 3.0. Predicting contact points with various versions of autoencoder has been done in suction cup [1], parallel jaw gripper [2], and dexterous hand [3]. However, the provided evaluation does not sufficiently illustrate the benefit of the proposed pipeline.\n\nPresentation of this paper needs improvement. Key details were left out, such as the types of objects used, the method used for fitting function F, the specific autoencoder architecture, etc. There is no supplementary material and the paper website does not clarify the information. \n\nStrengths:\n\n1.\tWell-motivated problem with clear relevance to robotics\n\nWeaknesses:\n\n1.\tLack of empirical evaluation, especially real-world results\n\n2.\tMissing details in the presentation\n\n[1] Jiang, P., Oaki, J., Ishihara, Y., Ooga, J., Han, H., Sugahara, A., ... & Ogawa, A. (2022). Learning suction graspability considering grasp quality and robot reachability for bin-picking. Frontiers in Neurorobotics, 16, 806898.\n\n[2] Mousavian, A., Eppner, C., & Fox, D. (2019). 6-dof graspnet: Variational grasp generation for object manipulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2901-2910).\n\n[3] Wu, A., Guo, M., & Liu, C. K. (2022). Learning diverse and physically feasible dexterous grasps with generative model and bilevel optimization. arXiv preprint arXiv:2207.00195.", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed."}, "extra_text": {}}]

Author rebuttal:

Meta-review: Following the rebuttal, the authors have actively made improvements to the paper since the initial manuscript submission. These improvements have led to a larger dataset, a better model, and more comprehensive experiments in simulated and real-world scenarios. It receives four **Weak Accept**. Therefore I would like to recommend an Acceptance. 

Please revise the paper in line with the rebuttal discussion for the camera-ready submission.

Strengths:

- The work tackles a challenging and relevant problem in suction cup grasping.

- Well-motivated problem with clear relevance to robotics

- A novel, more realistic approach to simulating suction cup grasps.

- The paper is well-written and structured

Weaknesses:

- Lack of empirical real-world evaluation, especially more complex scenarios

- The collection of objects mainly comprises cylinders and box-like items, which is quite limiting. It's recommended to include more commonplace objects as seen in other research papers on grasping.
Decision: Accept (Poster)