Volumetric-based Contact Point Detection for 7-DoF Grasping
Authors: Junhao Cai
Jingcheng Su
Zida Zhou
Hui Cheng
Qifeng Chen
Michael Y Wang
Venue: CoRL 2022 Poster (2022)
Keywords: Contact point detection
7-DoF grasping
General object grasping

Abstract: In this paper, we propose a novel grasp pipeline based on contact point detection on the truncated signed distance function (TSDF) volume to achieve closed-loop 7-degree-of-freedom (7-DoF) grasping on cluttered environments. The key aspects of our method are that 1) the proposed pipeline exploits the TSDF volume in terms of multi-view fusion, contact-point sampling and evaluation, and collision checking, which provides reliable and collision-free 7-DoF gripper poses with real-time performance; 2) the contact-based pose representation effectively eliminates the ambiguity introduced by the normal-based methods, which provides a more precise and flexible solution. Extensive simulated and real-robot experiments demonstrate that the proposed pipeline can select more antipodal and stable grasp poses and outperforms normal-based baselines in terms of the grasp success rate in both simulated and physical scenarios. Code and data are available at https://github.com/caijunhao/vcpd

Volumetric-based Contact Point Detection for 7-DoF Grasping
Junhao Cail.2 Jingeheng Su Zida Zhou Hui Cheng² Qifeng Chen' Michael Yu Wang
'The Hong Kong University of Science and Technology
PKKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen SSnn Yat-sen University *Monash University
Abstract: In this paper, propose novel grasp pipeline based on contact point detection on the truncated signed distance function (TSDF) volume to achieve closed-loop sedegree-of-freedee
(7-DoF) grasping on cluttered environments. The key aspects of our method are that 1) the proposed pipeline exploits the TSDF volume terms of multi-view fusion, contact-point sampling and evaluation, and collision checking, which provides reliable and collision-free 7-DoF gripper poses with real-time performance; 2) the contact-based pose representation effectively eliminates the ambiguity introduced by the normal- based methods, which pro- vides a more precise and flexible solution. Extensive simulated and real-robot experiments demonstrate that the proposed pipeline can select more antipodal and stable grasp poses and outperforms normal-based baselines in terms of the grasp success rate in both simulated and physical scenarios. Code and data are available at https::/ //gs //tgttttt......//// www.huu..ooooeccooo cttccciicccciaiiacaaa jaihho/oo/
Keywords: Contact point detection, 7-DoF grasping, General object grasping
Introduction
Assionbbased grasping is one of the most basic but important tasks in the robotics community. It is regarded as operation primitive, and benefits a robot in handling more complicated manipula- tion applications [1, 2, 3]. Although this prob- lem has been widely researched, remains chal- lenging, especially when the robot is confronted with general object grasping in cluttered environ- ments [4. 5, 6, 7]. To deal with this problem, most previous works have tried to evaluate the 6- degree-of of-freddom (6-DoF) pose (i.e., the position and orientation) of the end-effector given the vi- sual observation of the scene [8, 9, 7, 10, 11, 12], which provides more flexible solutions compared with planar-based methods [13, 5, 14, 6]. However, most of the existing 6-DoF approaches suffer from three main limitations in terms of the pose representation and visual observation: 1) The grip- per pose relies heavily on the assumptions that the end-point position of the gripper is estimated from position located on the object surface (e.g., the red sphere on the left of Figure 1) and that the approach direction is parallel to the corresponding surface normal (e.g., the blue line in Fig- ure 1) [8, 10, 11, 15, 16, 17, 18]. However, the first assumption may introduce position shift since most of the time the surface position is not exactly the same as the end-point position of the gripper (e.g., the black sphere on the left of Figure 1), which may result in failure when we empirically set the approach distance based on the surface position. Meanwhile, the second assumption may lead to no solution being found when all of the contact surface pairs the normal direction are not parallel to each other. 2) Existing works mainly focus on estimating the gripper poses based on single observation of the scene such as an RGB-D image or a point cloud [10. 19, 11, 20), which is visually insufficient, especially when tackling complex scenarios such as performing grasping in
Figure 1:
6-DoF grasping based on left: sur- face normal and vertex or right: contact points.
6th Conference on Robot Learning (CoRI. 2022), Auckland, New Zealand.

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(b)
(c)
(e)
(I)
Figure 2: Overview of the grasp pipeline. (a) visualizes the views where the vision sensor captures the depth maps. (b) is the mesh generated from the TSDF volume. (c) visualizes the potential contact pairs sampled by the proposed sampling and matching heuristics. Red dots stand for the contact points generated by the Marching Cubes algorithm. The green dots denote matched contacts retrieved from the intersections between the grasp vectors and the isosurface of the TSDF volume. After the evaluation of grasp quality for each contact pair, the top 3% of high-quality pairs are selected, and collision checking is performed by rotating the gripper in the grasp direction on the TSDF volume, which are shown in (d) and (e), respectively. The final collision-free gripper pose will be obtained, as shown in (f).
cluttered environment. 3) Due to the partial observation, directly conducting collision checking nontrivial. Although learning-bast based collision avoidance be leveraged on the single-view data, large amounts of training data are required and the performance might degenerate when performing grasping a different scene [10, 21, 22].
To resolve the limitations of current 6-DoF grasping methods, we propose a novel grasp pose detec- tion method based on detecting contact point pairs in the truncated signed distance function (TSDF) volume estimate both the 6-D pose and the width of the gripper, which we denote as 7-DoF grasping. Figure 2 illustrates the proposed pipeline. Our approach is closely related to the method proposed by Cai et [12], while we cireumvent the normal-based assumption and turn to detect- ing contact point pairs of the parallel-jaw gripper. The TSDF volume, discretized representation of the scene where each voxel denotes signed distance from current position to the closest object sur- face, allows multi-view fusion and provides more comprehensive shape information of objects that are essential for sampling and matching the contact point pairs. contact point quality evaluation network is also proposed to predict the grasp quality of contact points in a pair- wair manner. Based on the evaluation results and the TSDF volume, post-processing operations, including volumetric be conducted efficiently to determine the approach vec- tor [20] and width of the gripper alongside the contact point pair.
collision checking and pose clustering,
This novel grasping pipeline has three key characteristics: 1) The contact-based formulation, which directly provides the position of the grasp center and the grasp direction, can avoid the assumptions required by the normal-bact based methods [23, 24, 12]. 2) Instead of treating 3-D points of a single-

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view point cloud as grasp contact candidates, this pipeline allows us to sample potential contacts from a TSDF volume, which provides more comprehensive scene observation and facilitates reliable sampling and matching of the contact point pairs. 3) Based on the TSDF volume and contact point evaluation network, collision-free poses with high grasp qualities can obtained iteratively, guiding the robot to approach the target pose. Due to the eye-in-hand configuration, the vision sensor can capture different observations of the scene and thus enrich the information of the TSDF volume on the fly instead of multiple viewpoints being manually set beforehand [8, 23, 24, 25J. Therefore, the entire grasp pipeline can be executed in a closed-loop manner.
In summary, our main contributions are the following:
A novel grasp pose estimation method based on contact point detection and evaluation the TSDF volume.
The contact point detection module can be integrated into the volumetric grasp pipeline, which allows collision-free 7-DoF closed-loop grasping.
Extensive simulation and physical experiments demonstrate the superiority of the proposed contact-based method in terms of grasp success rate, antipodal score, and collision-free rate com- pared with normal- based ones [8, 23, 12].
2 Related Work
Pose representation. Many previous works have focused on resolving the grasping problem in planar manner, which determines the gripper pose by estimating the position and the grasp angle of the gripper the vertical direction [13, 26, 5, 14, 6]. However, this formulation inevitably restricts the ways objects could be grasped. In contrast, recent works have mainly favored the use of stereo data such as a point cloud or depth map to predict 6-DoF gripper poses, which can provide more versatile solutions when handling complex scenarios [27, 7, 8, 11, 10, 23, 12, 17, 16, 18, 15]. Many of them use the surface normals of the objects as prior of the approach direction when synthesizing grasp samples during data collection. Although this approach performs well in most cases, the performance might degenerate when there are no antipodal contact points constrained by surface normals. In addition, an ambiguity issue is also introduced by the normal-based methods, and researchers have resorted to performing angle discretization to eliminate the problem [12, 11].
Methods based on evaluating contact points have also been proposed [25, 20J. The contact-based methods directly regard the points of a point cloud as contact candidates and turn estimating the grasp qualities of potential contact pairs. This formulation effectively circumvents the normal assumption, and directly obtains the end-point position of the gripper. However, existing contact- based methods attempt to find graspable contact pairs only on partial view of the scene, which might ill-posed since an antipodal contact point pair commonly exists on two opposite sides of an object, and only single view of the scene might not include sufficient information.
Scene observation. Early works in vision-based grasping paid more attention to extracting shared graspable features for all objects from RGB images [13, 4, 26, 28]. Because of the explicit 3-D information of the scene and smaller gap for sim-to-real transfer, recent works have preferred to use depth maps or point cloud to conduct pose estimation [8, 14, 5, 17, 16, 6, 18]. However, these kinds of scene representations only reflect off the surface of the object, while an informative observation should naturally indicate both the geometric shape of each object and the spatial relationships be- tween objects and the free space. Therefore, methods based on SDF volume have been proposed in which multiple views of depth map can be fused into persistent 3D data structure, providing a more comprehensive representation of the scene that explicitly indicates both the occupied and free space [23, 24, 12].
3 Method
3.1 Problem Definition
In this work, we consider the clutter removal task where robot tries to pick any object out from the workspace one by one when all the objects are stacked together. All the objects are unseen by the model, and we can only observe the scene through vision sensor attached to end-effector.
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Suppose at time t, t, we capture t frames of depth maps I, = {1 11 with corresponding poses from the base to camera T7, {oTī oi and the camera intrinsic matrix K. The TSDF volume can be computed by TSDF fusion [29], which is formulated as V, ftxdf (I,, b T; K). Our goal is to estimate the optimal contact pair (Pr, and its corre- sponding grasp vector gf and approach vector aj [20] from V. The pose and width of the gripper can be further computed from the quadri-tuple (pi,2) pi 9e af), denoted as
02.0 = [e],,
Figure Pose repre-
6. RR = g6 x di D6- ail:
(1) sentation of the gripper.
e = (p) + +12))/2
where RRe denotes the rotation from the base to gripper frame, and of the end-point position of the gripper. The distance of two contact points can also determine the width of the gripper, which is computed as ||p p 22 An example of the gripper pose is shown in Figure 3.
To achieve this objective, a comprehensive volumetric-basee grasp pipeline, including contact pair sampling and matching, grasp quality evaluation for contact pairs, and approach vector selection by collision checking. is proposed to perform the closed-loop grasping. An overview of the grasp pipeline shown in Figure
3.2 Contact Pair Sampling
For simplicity, a schematic illustration of con-
tact point detection 2-D space is shown in
Figure 4. Given the current TSDF volume V.,
we execute the Marching Cubes algorithm [30] to retrieve the isosurface of the scene, which is represented as mesh set including the surface vertices P. {PI..,... ..., } and their corre- sponding vertex normals G. = {gi,e- IN.,). J. The sampled vertices are considered as the po- tential contact points between one side of the fingertips and the objects, which are repre- sented by the red dots in Figure 4. Moreover, the set of vertex normals determines the grasp direction of the gripper. We implement these settings based on the antipodal grasp rule [31], which says that to achieve stable grasp, the grasp direction of parallel-jaw gripper should be parallel to the surface normals of the con- tact points. With the above heuristic, we can further extrude all the grasp vectors in the in- verse direction to obtain another set of contact points P .p........ ....'''.. } by finding the zero- value voxels in the TSDF volume. We neglect the samples that cannot find their matched con-
Marching Cubes
MI.P
Figure 4: Illustration of contact point detection. Red dots and purple arrows are vertices and their surface normals respectively, as extracted by the Marching Cubes algorithm. Green dashed lines the extrusion of the surface normals, and their intersections with the object are marked as green dots.
tact points in the inverse grasp direction or whose distances to their matched points are greater than the gripper width. For simplicity, we consistently use the symbol N as the final number of sampled contact pairs in the subsequent sections.
Compared with existing works, our detection pipeline leads to 3 benefits: 1) The contact point pairs are sampled from a more comprehensive visual representation, which is more reliable than those sampled from single point cloud. 2) The spatial information provided by the TSDF volume significantly simplifies the process of finding the matched contact points. 3) By making use of the surface normals, the sampled contact pairs are more prone to being antipodal, which is essential for stable grasping. Details about how to generate contact pairs in the TSDF volume will be illustrated in the appendix.

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3.3 Contact Point Network
Based on 3-D fully convolutional network (FCN) and multi-layer perceptron (MLP), we next propose a contact-point network (CPN) fa to evaluate the grasp quality pairwisely for all the contact pairs, which is also shown in Figure 4. We follow the network architecture proposed by [12] except that the last layer is replaced with a single-value output which ranges from [o, 1], representing the grasp quality, The spatial features of the scene and the geometric features of the objects are first extracted by the 3-D FCN. We then use the sampled contact pairs as indices to retrieve the grasp features from the 3-D feature maps. Finally, the retrieved features for each contact pair will be sent to the MLP module to predict the corresponding grasp quality. The entire process can be formulated as Q fo(V,, Pt, P/. Gc), where Qt E R represents the estimated grasp qualities for all the contact candidates. By leveraging the proposed CPN, we not only make the most of scene information provided by the TSDF volume but also circumvent redundant computation costs by only evaluating potential antipodal contact pairs.
3.4 Approach Vector Selection
So far, we have obtained the contact pairs with high grasp quality, which can determine the end- point positions and the grasp vectors according to Eqn. 1. However, we cannot directly deduce the approach vectors from the contact pairs. Benefiting from the convenient collision checking in the TSDF volume, we can select the approach vector for each contact pair by rotating the gripper mesh on the axis of the grasp vector. One example is shown in Figure 2(e).
Assume that we have the gripper mesh model my with its vertices denoted as P. = {pi,g- Pr,. For each contact pair (pi.e. P(..). we sample N₂ poses 6 .T {o :.:-........... 77,, ..t) with their posi- tions centered at the end-point position and the y axes of their orientation aligned the grasp vector. The 2 axes, uniformly sampled in the subspace perpendicular to y. denote the potential approach vec- tors. Based on the TSDF volume, the vertex set, and the sampled poses of the gripper, we can check whether the gripper will collide with the objects by transforming the vertices with the sampled pose and evaluating the signed distance values retrieved by the transformed vertices in the TSDF volume. The poses whose retrieved signed distance values are all greater than 0 (i.e., the transformed vertices are all located outside the objects) will be considered as feasible grasp poses. This operation can be denoted as 4,1 = fec.ady (,T, Pg. Vi), where 6 T S 6 TTT is the collision-free pose set for contact pair And the entire process can be summarized as .7." fo Tf TT where N is the number of graspable contact pairs and TT" contains all the feasible poses. Finally, the final grasp pose -TT" can be determined by performing non non-maximum suppression (NMS) operation [12] on 6 TT.
4 Data Generation
To train the proposed CPN, we require training data consisting of the TSDF volume of the scene, sampled contact pairs, and their corresponding grasp qualities. Such data can be generated in three steps: grasp analysis on a single mesh, label validation on the simulator, and scene construction. Visualization of the synthesized data is available in the appendix.
Grasp analysis on single mesh. Before generating the cluttered scenarios with multiple objects stacked on top of each other, we first sample potential contact pairs on every single mesh and perform grasp analysis on all the candidates. Specifically, for each object mesh m. = Fm, consider the vertices as contact points
rep-
resented as the set of vertices, faces, and vertex normals, between one of the fingertips and the object. The opposite contacts P/,, for another fingertip are generated by finding the intersections between the object and the rays cast in the inverse normal di- rection. For each contact pair (p), p²)). we next evaluate the grasp quality by computing the antipodal score [12] and validating the collision status between the scene (the object m,) and the gripper myg. with its poses 7 determined by the contact points and the uniformly sampled approach vectors. the antipodal score greater than the given threshold and any of the poses conditioned on that con- tact pair lead to collision- free grasp, regard the candidate as graspable. Therefore, the output is
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(a) Contact points with normals
(b) Positive contact pairs
(c) Negative contact pairs
Figure 5: Visualization of grasp analysis on single mesh.
a binary vector Qm; E {0, 1,m,, where each of the binary values is determined by
1, 8j 2 cos(ai) cos(022 and foc.sim (oT,", mgs 8 ,
9i 0 O, otherwise
(2)
where 8y is the antipodal score for contact pair (pj. an and ag are the given thresholds of angles between the contact normal and the grasp direction, and feasim provides collision checking between the gripper my and other meshes M, given the gripper poses 67,' and mesh poses .TMM, respectively. In this step, MM; = {m:}. Similar to Seasaf in Sec. 3.4, Jac.sim outputs the set of collision-free poses in o7,"
Label validation. Although analytical measures provide effective ways to generate grasp labels, they are not always reliable when such heuristics are transferred to the physical environment. Due to the rapid development of physics simulation, many works make use of a simulator to generate physically realistic grasp labels using a simulated robot arm validate the graspability of the poses generated from analytical heuristics [15, 10, 32, 33]. In this work, we validate the grasp labels (Qm:) } by leveraging the Isaac Gym simulator [34], which can thousands of environments in parallel with GPU accelcration. We follow the settings proposed by [33] to evaluate the reliability of all the candidate poses. One of the labeled meshes shown in Figure 5.
Scene construction. Given sets the mesh contact pair, and grasp quality, we can build stacked scenes with labeled contact points. To achieve this, first randomly pick and drop the meshes into a tray. The depth maps and their camera poses are respectively recorded as Iy and T7. All the contact pairs then transferred to the scene according to the mesh poses, and collision checking is executed based on sim- Candidates that are both graspable and collision-fre will be considered positive samples. Moreover, negative samples collected based on three patterns, i.e., contact pairs 1) whose grasp qualities after label validation are 0, 2) whose grasp qualities after label validation are while all the sampled poses on those contacts cause collisions in the scene, and 3) that are from positive samples but perform random permuting and re-matching, which generates a new set of contact point pairs. Finally, the depth maps ZT with corresponding camera poses à TT merge into the TSDF volume by TSDF fusion V/2 ftsap (Ie. save volumes that integrate different numbers of depth maps. Despite the multi-view observation, some areas containing contact points may be invisible. Therefore, we neglect samples which have signed distance values of the contact points larger than specific threshold.
where ranges from 0 to T such that we can
Experiments
This section presents extensive simulation and real-world experiments evaluate the proposed grasp detection pipeline. We aim to validate 1) the superiority of the proposed contact-based method under the stacked scenarios compared with normal-base basclines and 2) the performance of the approach in a physical platform under different clutter removal scenarios. To this end, we compare the proposed method with three normal- based approaches, GPD [8], VGN [23], and VPN [12]. GPD performs grasp detection in point cloud, while VGN and VPN detect grasp poses from the TSDF volume.
5.1 Evaluation Metrics
In this work, we use four metries to evaluate the estimated grasp poses: 1) antipodal score (AS): 2 cos(aa) cos(az)) where 0₁ and 02 are the angles of the grasp direction and contact normals

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of the parallel-jaw gripper, 2) collision-free rate (CFR): the percentage of grasp poses that do not cause collisions to grasp attempts on cluttered scenes, 3) grasp success rate (GSR): the percentage of successful grasps compared to total grasp attempts, and 4) grasp completion rate (GCR): the percentage of objects removed by the robot compared to the total number of grasp items. Follow- ing [12], we will manually remove any object that successively fails to be grasped three times.
5.2 Implementation Details
As demonstrated by Cai et al. [12], primitive-shaped objects can provide precise grasp annotation, and those grasp patterns are transferred well to unknown objects. We thus keep using the 191 primitive-shaped objects, including spheres, cubes, ellipsoids, cylinders, and building blocks re- leased by [35] generate the dataset. For scene construction, we follow the settings of VPN [12] to generate 8000 cluttered scenes with 5, 10, 15, and 20 objects stacked in the tray, respectively. For TSDF generation, four TSDF volumes are collected when 5, 10, 14, and 19 frames of depth images are integrated into the volume. All the training data are collected in PyBullet [36], and no real data are required.
During performance demonstration, primitive-shaped, procedural [37], and Kit [38] object sets are used for the AS and CFR evaluation in PyBullet and GSR evaluation in Isaac Gym. In the physical experiment, 32 unseen objects, comprising 8 fruits and 4 cups from the YCB object set [39]. 8 household objects from YCB [39] and GraspNet [7], and 12 adversarial objects from DexNet [5] are used. Moreover, we use the Franka Emika robot arm with the RealSense D435 and parallel-jaw gripper to perform grasp demonstrations. The vision sensor is mounted on the end-effector with its pose from the base to camera calibrated [40].
More details about the data, network architecture, parameter configuration, test objects, sampled cluttered scenes, and physical setting are posted in the appendix.
5.3 Simulation Experiments
We first investigate the antipodal scores and collision-free rates of the different methods by finding the contacts and collision status of the gripper given the pose estimated by the models in PyBullet. To demonstrate the robustness of the proposed methods, we respectively compute the average of the metrics on every 1000 scenes with 5, 10, 15, or 20 objects stacked together. We randomly render 20 depth maps from different viewpoints for each scene generate the TSDF volume.
Table 1: AS / CFR /SR
V3
VPN [12]
VPN+GPRN [12]
CPN (Ours)
09944993.79997 0.664747.07780
0,987 98.1 100.0
Primitive
10
0.629/33.4763
0.958/915//99
0.960/ / 90.2/
0.986 / 95.9 99.9
15
0.631 / /2.88/628
0.960/90.1 /99.6
0.964 / /9.5
0.985 / /6.5 99.8
20
0.621 /27.2/22//// 58.8
0.962 84.0
0.985 / 95.4 99.7
0.638771.17811
039177888799
944,
0.9217 7 89,2792.9
73.70793.37 98.8
Kit
15
10
0.614/44.0 /4.0/ S0.1
0.621 / 56.6 84.0
0.952
0.973/85.5/99 / 93.8
95.6
0.953 78.7 / 99.22
0.946 / 84.4 16.1
0.980 / 91.8/ 99.7
0.980 / 93.0 99.9
20
0.608 / 34.0/ 79.1
0.946 0.946/76.2 /5.0
0.947 74.4 16.00
0.980 / 90.0 99.4
5
0,4474779771
0.801769.3 979.99
0.8307 793.
0.9457 7 89.6 98,4
Procedual
10
0,470 51.3 / 68.1
0.794/ 67.2/ 90.7
0.818 / 66.9 92.2
0.939 88. 98.6
15
0,483 0193//33.4 /3344//33333
0.796 / 66.0/ 90.1
0.814 / /6.7/66888 89.8
0.940 / 88.6 98.7
20
0,471 / 65.6/65.9
0.785 / 62.9 / 89.4
0.801 / 62.0 88.6
0.942 / 90.3 98.1
The results, reported in Table show that 1) the proposed contact-based method achieves state-of- the-art performance in terms of AS compared with the baselines, which implies that it can generate gripper poses that are more antipodal, and shows the superiority of the proposed contact-based method; 2) our method achieves similar performance on both the seen (primitive) and unseen (Kit and procedual) object sets, which demonstrates its generalization ability and robustness; and 3) our method outperforms the baselines the CFR. We thus believe that the gripper widths estimated from the contact points and TSDF volume effectively decrease the risk of collision with other ob- jects.
To further evaluate the reliability of the proposed method, we measure the GSR on all the object sets in the Isaac Gym simulator. The scene configuration and the number of depth maps are the same as in PyBullet. We randomly generate 1000 scenes with different levels of clutter and conduct one grasp attempt for each scene. The results, also reported in Table 1, demonstrate that our method can detect robust and reliable poses for the gripper on scenes with different clutter levels. More results and analyses of grasp experiments on other object sets are posted in the appendix.
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Figure 6: Demonstration of real robot grasping,
5.4 Real-robot Experiments
We design a series of clutter removal experi- ments on the four types of objects mentioned in Sec. 5.2. We select 8 (4 objects only for cup- shaped objects) objects of the same type for each experiment and randomly place them into the workspace to form a stacked scene. We then execute the grasp according to the pose estimated by the models. We compare our method with GPD [8], VGN [23], and VPN [12]. Concretely, GPD estimates the gripper pose on a point cloud. For VGN, we follow the settings from [23] to generate the TSDF volume by moving the vision sensor to four viewpoints, while for VPN and our method, we keep using the VPN setting that the TSDF fusion and pose estimation are conducted on the fly, and the entire grasp trial is run in closed-loop manner.
Table 2: GSR GCR
Mugs [4 x 5)
Fruits (8 x 3)
Household (8 x 5)
Adversaria (8 x 5]
CPD [8]
58,067 150.00
43.597 K5,00
68.427 97.50
70.91797.55
VGN [25]
80,00/ 100.0
56.25 90.00
39.74 / 77.50
41.56/ 80.00
VPN [12]
100.0/ 100.0
84.78 97.50
86.97 100.0
73.85/97.55
VPN+GPRN [12]
100.0 100,0
81.63 100.0
90.91 / 100,0
78.43 100.0
CPN (Ours)
100.0 100.0
93.02/ 100.0
95.24/ / 100.0
85.11/ 100.0
(m X T1) represents m7 objects with 71 test rounds.
The GSR and GCR listed in Table 2 suggest that 1) our method achieves of-th-- perfor- mance on all types of grasp items, performing especially well on spherical objects, such as fruits, compared with the normal-based methods, which further demonstrates the ability to obtain antipo- dal grasp poses; 2) though trained with only primitive-shaped objects and synthesized images, the proposed pipeline can be directly applied to handling unknown objects in physical environments, which implies that the generated scenes are complex enough for the model to learn to detect gras- pable regions; and 3) although the viewpoints of the vision sensor are different from those set in the simulator during the TSDF fusion process, all the modules that require the TSDF volume still perform well, which shows the robustness of our method.
6 Conclusion and Limitations
This paper presents a novel vision-bases grasp pipeline for detecting and evaluating contact pairs on the TSDF volume. The key advantages of this work are 1) the use of the TSDF volume, comprehensive representation of the scene that allows reliable sampling of antipodal contact pairs, geometry-aware grasp quality inference, and collision checking with real-time performance, and 2) contact-based pose representation that effectively circumvents the ambiguity introduced by the normal-based approaches. Simulation experiments show that our method can efficiently avoid col- lision and compute more antipodal grasp poses. Clutter removal experiments show that our method trained with only synthesized data performs significantly better than other volumetric methods on all objects, which further demonstrates its ability of generalization.
Despite the advantages presented by the TSDF volume, this volumetric- based pipeline suffers lim- itations when handling slim- or flat-shaped objects in the physical environment. Due to the noise introduced by the consumer-level sensor and the truncated margin of the TSDF volume, the slim parts of the objects might be missing or the side surfaces of flat objects may be prone smoothing during the TSDF fusion, which leads to no contact points being found. To overcome this limitation, a more robust heuristic to infer the contact points and the contact normals will be considered in future work. More details about the failure cases are illustrated in the appendix.
Moreover, this work can only infer contact points for parallel-jaw gripper. It would be a promising direction to investigate how to apply this contact-based pipeline for multi-finge grippers.
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Acknowledgments
This work was supported in part by the Hetao Shenzhen-Hong Kong Science and Technology In- novation Cooperation Zone under the project (000B-KCYYB-20200S The authors would like to thank all the reviewers for their constructive feedback and for helping us make our paper more organized.
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Reviews:
[{"review_id": "tS_CkNQ1nBK", "reviewer": "Reviewer_eHii", "summary": "This paper proposes a grasp generation pipeline that reasons over a TSDF volume. To achieve this, the pipeline learns a grasp analysis model that reasons directly over meshes. This approach is validated over a wide range of experiments grasping objects in cluttered scenes. This pipeline significantly outperforms common baselines from normal-based grasp selection in both real-world and simulated experiments.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is well written and overall easy to follow.\n2. The new volumetric approach to grasping leads to major improvements in performance in comparison to normal-based approaches\n3. This pipeline is validated over a large number of experiments, both real-world and simulated.\n4. The paper is very clear and straightforward on its limitation and areas for improvement\n\nWeaknesses:\n\n1. The authors could improve the visual depiction of the pipeline for readers unfamiliar with the state-of-the-art grasping pipelines.", "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": "", "summary_of_recommendation": "This paper presents a novel contribution to the field of grasping. The approach builds on top of previous work on volumetric grasping and demonstrates how this pipeline outperforms previous work on a wide array of experiments. The paper is well written, is technically correct, and a relevant contribution to the manipulation community. The reviewer recommends its acceptance.", "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": "", "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": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {"issues": "My only issues with this paper lie in the presentation:\n\n1. Figure 2. could be improved to better visually depict the proposed pipeline for unfamiliar readers.\n2. Figure 4. can be confusing at first and is not well labeled.\n3. The authors remark on the fact that their approach is trained exclusively on synthetic data, which is very impressive. However, this seems to be common among other pipelines as well. The reviewers should be careful with this statement.\n4. The paper does not show any images of the real results or experimental setup. While not critical, it would be helpful to understand the difference between the synthetic training setup and the experimental setup."}}, {"review_id": "qiVLkiqS8z7", "reviewer": "Reviewer_cFdk", "summary": "This paper proposes a grasping-in-clutter framework for computing grasping contact pairs from a Truncated Signed Distance Function (TSDF). The paper argues that TSDFs capture more of the scene's geometry and the contact pair formulation removes the normal-based assumption present in other frameworks. From the TSDF, the Marching Cubes algorithm generates a mesh whose vertices serve as a grasp candidates. The framework next uses an antipodal grasping rule to find valid contact pairs, which are fed into a Contact-Point Network (CPN) that evaluates them for grasp quality. The CPN also takes the TSDF as input, allowing it to account for scene information. Finally, the gripper is collision checked along various approach vectors to find a valid one. To train the CPN, a large set of contact points are scored analytically and in simulation, based on whether they are graspability and collision-free. The method is compared against three other normal-based approaches, two of which use TSDFs.  ", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "This paper focuses on a very relevant application area (grasping in clutter) and by using TSDFs the framework is able to factor in collision information about the scene. Removing the assumption to grasp normal to the object's surface could enable a wide range of grasping. The paper is well-organized and the related work highlights the motivation for the methodology. The experimental section compares to three state-of-the-art methods across a large set of simulation experiments and a clutter removal experiment. The discussion on how TSDF-based methods can struggle when grasping thin objects was particularly interesting.\n\nThe paper proposes a closed-loop grasping pipeline stating that as the robot moves the input from the sensor can be used to improve \"the performance of the contact point detection\". However it is not clear how the performance is improved over time. How is new information incorporated in? Would the grasping point ever change? Is the TSDF updated? Currently it is not clear how the closed-loop pipeline is implemented.\n\nParticularly because two of the compared methods (VGN and VPN) use TSDF, it would be great to get further insight on how the proposed method uses the TSDF in a way that leads to improved performance. This could help tease out where the improvement comes from and how the aspects of the proposed approach connect back to the performance. ", "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": "", "summary_of_recommendation": "I am weakly recommending the acceptance of this paper. The paper builds on existing work to bring together several interesting ideas in order to enable a more generic form of grasping. The experimental section is fairly extensive and compares against several other relevant algorithms. The closed-loop pipeline is currently not clearly explained and there are a few clarifications (described below) that should be made.  ", "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": "", "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": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {"issues": "Connecting to what was stated above, it would strengthen the paper to clarify how the closed-loop pipeline is implemented. This seems particularly important given the emphasis of the closed-loop nature in the introduction. \n\nRelated to this, Sec 3.5 makes the comment that the modules \"can\" be implemented with GPU acceleration such that the framework can be efficiently executed closed-loop. If the experiments were done without this acceleration, can the process still be executed closed-loop? What aspects of the pipeline are a bottleneck? \n\nWithin the introduction, I didn't quite understand what meant by \"first assumption may introduce position shift since most of the time the surface position is not exactly the same as the end-point position of the gripper,\". Is this that the surface position is not the desired position of the gripper during the grasp? What is meant by position shift, is this during the grasping process? Perhaps clarifying the wording, or further using Fig. 1 as an example may help clarify this motivational assumption. \n\nA few smaller comments, questions, notes: \n- Making the annotations on Fig. 1 larger would make them easier to see. \n- The appendix discusses that the step size of the Marching Cubes algorithm was set such that the number of vertices is around 1000-8000, which is \"sufficient for covering the entire scene\". How was this number chosen and how would changing this parameter impact the performance of the algorithm? \n- Sec 5.1 mentions that any object that could not be grasped three times was manually removed. Where the type of objects that needed to be removed flat-shaped objects, as discussed in the limitations, or were there other classes of objects that the grasping systems struggled with? \n- Given that the best performance is bolded in Table 1, it would maintain consistency to do a similar bolding in Table 2. \n"}}, {"review_id": "foSx4lgQeo2", "reviewer": "Reviewer_EzWb", "summary": "The paper presents a 7-dof grasp point detection method. The proposed method considers the TSDF volume and trains a network to detect the grasp point using simulators. The paper validates the method by comparing the method with other methods using various objects in simulations and real robot experiments.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n- The introduction describes clearly the background and contribution of the paper.\n- The related work section introduces related topics and papers and compares them with the proposed method.\n- The method section, data section, and experiment sections clearly demonstrate the method, data, and experiments, respectively.\n- The limitation part describes the limitation of the method precisely.\n\nWeakness\n- All issues have been resolved in the revision.", "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": "", "summary_of_recommendation": "This reviewer recommends Weak Accept. The paper is written clearly and experimental results support the claim of the paper well. However, the reviewer is not familiar with the field of the paper and is not sure how novel the proposed method is and how difficult the problem of the paper is.", "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": "", "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": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "issues": "All issues have been resolved in the revision.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {}}, {"review_id": "80Aay_MHn7W", "reviewer": "Reviewer_AfSj", "summary": "This paper proposes an algorithm for object grasping in clutter from a truncated signed distance function (TSDF) volume scene representation. The algorithm represents a grasp using a pair of points where the two fingers of a parallel jaw gripper are supposed to contact the object i.e. antipodal contact points. One of the pair of antipodal points is obtained by sampling from the input TSDF, and the other one by extending a vector backwards along the surface normal and intersecting it with the TSDF zero surface. Training is performed by generating data from a set of mesh objects arranged into cluttered scenes, labelled with theoretical antipodal score.\n\nExperiments performed in simulation and on a real robot measure grasp success (GSR) and the success rate of clearing a clutter of objects (3 tries allowed per object to pick it up) (GCR).", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "##  Strengths\n- The paper is well written and understandable, figures are useful\n- While this paper is not the first to introduce the idea of operating on a TSDF instead of a single image, the introduction section motivates the use of TSDF well\n- Real robot experiments are conducted to verify the simulation results\n\n## Weaknesses\n- It is not clear how different this paper is from others that also operate on a (T)SDF volume, especially [12]\n- One key difference is that [12] predicts 6-DoF gripper poses instead of contact point pairs as proposed here. However, the current experiments showing incremental performance improvement over [12] do not control for other factors, hence it cannot be said that the performance improvement is due to the grasp representation proposed here. For example, [12] uses an 8-layer network, while the network in this paper is more than twice deep (see appendix). EDIT: This was addressed in the authors' response below.\n\n### Smaller points\n- L15 \"dexterous\": this paper does not address dextrous manipulation\n- L35-36: meaning unclear, difficult sentence to understand\n- Section 3.5 is mostly redundant, the space can be used for other purposes e.g. images of real robot execution \n- L189: \"closed loop grasping pipeline\": the pipeline described here is not closed loop - that would require the grasp sucess/failure signals influencing future grasps.\n- L240: will the randomly paired points be antipodal? if not, why do this, because real world points will all be antipodal\n- Quantitative comparisons to baselines in Table 3 are technically not comparable because they do not use the same test sets / scenes. This needs to be discussed in the paper if the current presentation is maintained.\n- Supplementary material not referenced", "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": "", "summary_of_recommendation": "I am voting to reject this paper because it is unclear whether the incremental performance improvement over [12] is because of the proposed grasp representation or a deeper network.\nEDIT: Raising my rating to Weak Accept because the authors' response addresses my main concern.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     ", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "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": "", "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": "", "extra_scores": {"impact": "1: Not very relevant to the robot learning community.", "issues": "Ablation study experiments, especially to tease apart the effect of the proposed grasp representation.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)