Fast and Precise Detection of Object Grasping Positions with Eigenvalue Templates

Mano, Kousuke; Hasegawa, Tetsuya; Yamashita, Takayoshi; Fujiyoshi, Hironobu; Domae, Yukiyasu

doi:10.1109/icra.2019.8793830

Cited by 4 publications

(7 citation statements)

References 15 publications

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“…Suction grippers need planar surfaces close to the centroid of objects to grasp effectively [16]. Various approaches to estimate the grasp position have been developed, including those using only depth images [12], [17], or making use of point clouds [15], [16], and those using RGB images [8], [18].…”

Section: Related Workmentioning

confidence: 99%

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Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data

Pattar

Hirakawa

Yamashita

et al. 2022

IEICE Trans. Inf. & Syst.

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Predicting the grasping point accurately and quickly is crucial for successful robotic manipulation. However, to commercially deploy a robot, such as a dishwasher robot in a commercial kitchen, we also need to consider the constraints of limited usable resources. We present a deep learning method to predict the grasp position when using a single suction gripper for picking up objects. The proposed method is based on a shallow network to enable lower training costs and efficient inference on limited resources. Costs are further reduced by collecting data in a custombuilt synthetic environment. For evaluating the proposed method, we developed a system that models a commercial kitchen for a dishwasher robot to manipulate symmetric objects. We tested our method against a modelfitting method and an algorithm-based method in our developed commercial kitchen environment and found that a shallow network trained with only the synthetic data achieves high accuracy. We also demonstrate the practicality of using a shallow network in sequence with an object detector for ease of training, prediction speed, low computation cost, and easier debugging.

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Section: Related Workmentioning

confidence: 99%

“…The fast grasp evaluator (FGE) [17] is a template matching method for depth images that uses only the depth image and templates of the gripper to find the optimum grasping points. Its process flow shown in Fig.…”

Section: Fast Grasp Evaluatormentioning

confidence: 99%

Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data

Pattar

Hirakawa

Yamashita

et al. 2022

IEICE Trans. Inf. & Syst.

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“…Grasping point detection algorithms [15][16][17] are robust methods to find graspable points from cluttered scenes. Fast Graspability Evaluation (FGE) [17] is a practical-use method that finds graspable points by convolving the 2D gripper model and the parts surfaces on a depth map.…”

Section: Kitting Task Proceduresmentioning

confidence: 99%

Team O2AS at the world robot summit 2018: an approach to robotic kitting and assembly tasks using general purpose grippers and tools

et al. 2020

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In this article, we propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers. Instead of specialized end effectors, it uses its two-finger grippers to grasp and hold tools to perform subtasks such as screwing and suctioning. A third gripper is used as a precision picking and centering tool, and uses in-built passive compliance to compensate for small position errors and uncertainty. A novel grasp point detection for bin picking is described for the kitting task, using a single depth map. Using the proposed system we competed in the Assembly Challenge of the Industrial Robotics Category of the World Robot Challenge at the World Robot Summit 2018, obtaining 4th place and the SICE award for lean design and versatile tool use. We show the effectiveness of our approach through experiments performed during the competition.

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“…Given an RGB-D image, the grasp configuration for a jaw gripper (Kumra and Kanan, 2017 ; Chu et al, 2018 ; Zhang et al, 2019 ) or a vacuum gripper (Araki et al, 2020 ; Jiang et al, 2020 ) can be directly predicted using a deep convolutional neural network (DCNN). Learning was extended from points to regions by Domae et al ( 2014 ) and Mano et al ( 2019 ), who proposed a convolution-based method in which the hand shape mask is convolved with the depth mask to obtain the region of the grasp points. Matsumura et al ( 2019 ) later learned the peak among all regions for different hand orientations to detect a grasp point capable of avoiding multiple objects.…”

Section: Introductionmentioning

confidence: 99%

Learning Suction Graspability Considering Grasp Quality and Robot Reachability for Bin-Picking

Jiang

Oaki²,

Ishihara³

et al. 2022

Front. Neurorobot.

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Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of large dataset is expensive. To address this problem, images were generated by a physical simulator, and a physically inspired model (e.g., a contact model between a suction vacuum cup and object) was used as a grasp quality evaluation metric to annotate the synthesized images. However, this kind of contact model is complicated and requires parameter identification by experiments to ensure real world performance. In addition, previous studies have not considered manipulator reachability such as when a grasp configuration with high grasp quality is unable to reach the target due to collisions or the physical limitations of the robot. In this study, we propose an intuitive geometric analytic-based grasp quality evaluation metric. We further incorporate a reachability evaluation metric. We annotate the pixel-wise grasp quality and reachability by the proposed evaluation metric on synthesized images in a simulator to train an auto-encoder–decoder called suction graspability U-Net++ (SG-U-Net++). Experiment results show that our intuitive grasp quality evaluation metric is competitive with a physically-inspired metric. Learning the reachability helps to reduce motion planning computation time by removing obviously unreachable candidates. The system achieves an overall picking speed of 560 PPH (pieces per hour).

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Fast and Precise Detection of Object Grasping Positions with Eigenvalue Templates

Cited by 4 publications

References 15 publications

Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data

Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data

Team O2AS at the world robot summit 2018: an approach to robotic kitting and assembly tasks using general purpose grippers and tools

Learning Suction Graspability Considering Grasp Quality and Robot Reachability for Bin-Picking

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