Affordance-Based Grasping Point Detection Using Graph Convolutional Networks for Industrial Bin-Picking Applications

Iriondo, Ander; Lazkano, Elena; Ansuategi, Ander

doi:10.3390/s21030816

Cited by 26 publications

(15 citation statements)

References 58 publications

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“…The existing state-of-the-art GCN-based techniques mostly focus on the gripper end-effector. In [ 118 ], a GCN-based method was proposed to predict object affordances for grippers and suction end-effectors in bin-picking situations. To overcome the existing generalization and scalability constraints, the grasping approach was extended to 6-DoF in a more flexible manner.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Researchers must seek ways to learn and estimate object affordances with fewer examples that have been manually annotated. Even though existing state-of-the-art GCN-based techniques focus on the gripper end-effector, a comparison of [ 118 ] with other methods in a bin-picking situation would be beneficial. Consequently, the grasping approach needs to be extended more flexibly to 6-DoF configurations to overcome the existing generalization and scalability constraints.…”

Section: Recommendationsmentioning

confidence: 99%

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Review of Learning-Based Robotic Manipulation in Cluttered Environments

Mohammed

Kwek

Chua

et al. 2022

Sensors

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Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

Review of Learning-Based Robotic Manipulation in Cluttered Environments

Mohammed

Kwek

Chua

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…There are different degrees of overlap and occlusion interference with the detection and perception of objects, yielding the failure of the robotic grasping task [1]. Bin-picking is challenging, attracting many domestic and foreign scholars [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A 6D Pose Estimation for Robotic Bin-Picking Using Point-Pair Features with Curvature (Cur-PPF)

Cui

et al. 2022

Sensors

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Pose estimation is a particularly important link in the task of robotic bin-picking. Its purpose is to obtain the 6D pose (3D position and 3D posture) of the target object. In real bin-picking scenarios, noise, overlap, and occlusion affect accuracy of pose estimation and lead to failure in robot grasping. In this paper, a new point-pair feature (PPF) descriptor is proposed, in which curvature information of point-pairs is introduced to strengthen feature description, and improves the point cloud matching rate. The proposed method also introduces an effective point cloud preprocessing, which extracts candidate targets in complex scenarios, and, thus, improves the overall computational efficiency. By combining with the curvature distribution, a weighted voting scheme is presented to further improve the accuracy of pose estimation. The experimental results performed on public data set and real scenarios show that the accuracy of the proposed method is much higher than that of the existing PPF method, and it is more efficient than the PPF method. The proposed method can be used for robotic bin-picking in real industrial scenarios.

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“…With regard to bin picking, however, a huge body of research is present. Based on image data, the most feasible grasp pose is estimated in order to pick objects from a bin [37][38][39][40][41]. Clearly, the application of reinforcement learning (RL) in vacuum-based handling has already shown great potential for technological improvements.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

et al. 2021

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Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation.

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Affordance-Based Grasping Point Detection Using Graph Convolutional Networks for Industrial Bin-Picking Applications

Cited by 26 publications

References 58 publications

Review of Learning-Based Robotic Manipulation in Cluttered Environments

Review of Learning-Based Robotic Manipulation in Cluttered Environments

A 6D Pose Estimation for Robotic Bin-Picking Using Point-Pair Features with Curvature (Cur-PPF)

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

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