DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration

Quality Assessment of Robotic Grasping Using Machine Regularized Leaning Algorithms

IEEE Sensors Letters ◽

10.1109/lsens.2020.2994166 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Rami Alkhatib ◽

Wajih Mechlawi ◽

Rabab Kawtharani

Keyword(s):

Quality Assessment ◽

Robotic Grasping

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Robotic grasping: from wrench space heuristics to deep learning policies

Robotics and Computer-Integrated Manufacturing ◽

10.1016/j.rcim.2021.102176 ◽

2021 ◽

Vol 71 ◽

pp. 102176

Author(s):

João Pedro Carvalho de Souza ◽

Luís F. Rocha ◽

Paulo Moura Oliveira ◽

A. Paulo Moreira ◽

José Boaventura-Cunha

Keyword(s):

Deep Learning ◽

Robotic Grasping ◽

Learning Policies

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No-Regret Shannon Entropy Regularized Neural Contextual Bandit Online Learning for Robotic Grasping

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9341123 ◽

2020 ◽

Author(s):

Kyungjae Lee ◽

Jaegu Choy ◽

Yunho Choi ◽

Hogun Kee ◽

Songhwai Oh

Keyword(s):

Online Learning ◽

Shannon Entropy ◽

Robotic Grasping

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Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9340777 ◽

2020 ◽

Author(s):

Sulabh Kumra ◽

Shirin Joshi ◽

Ferat Sahin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Robotic Grasping

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A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420985739 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142098573

Author(s):

Wenjie Geng ◽

Zhiqiang Cao ◽

Zhonghui Li ◽

Yingying Yu ◽

Fengshui Jing ◽

...

Keyword(s):

Principal Component ◽

Major Axis ◽

Target Object ◽

Potential Fields ◽

Single Shot ◽

Robotic Grasping ◽

Vertical Projection ◽

Convex Curves ◽

Functional Representation ◽

Continuous Potential

Vision-based grasping plays an important role in the robot providing better services. It is still challenging under disturbed scenes, where the target object cannot be directly grasped constrained by the interferences from other objects. In this article, a robotic grasping approach with firstly moving the interference objects is proposed based on elliptical cone-based potential fields. Single-shot multibox detector (SSD) is adopted to detect objects, and considering the scene complexity, Euclidean cluster is also employed to obtain the objects without being trained by SSD. And then, we acquire the vertical projection of the point cloud for each object. Considering that different objects have different shapes with respective orientation, the vertical projection is executed along its major axis acquired by the principal component analysis. On this basis, the minimum projected envelope rectangle of each object is obtained. To construct continuous potential field functions, an elliptical-based functional representation is introduced due to the better matching degree of the ellipse with the envelope rectangle among continuous closed convex curves. Guided by the design principles, including continuity, same-eccentricity equivalence, and monotonicity, the potential fields based on elliptical cone are designed. The current interference object to be grasped generates an attractive field, whereas other objects correspond to repulsive ones, and their resultant field is used to solve the best placement of the current interference object. The effectiveness of the proposed approach is verified by experiments.

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