GraspME - Grasp Manifold Estimator

Hager, Janik M.; Bauer, Ruben; Toussaint, Marc; Mainprice, Jim

doi:10.1109/ro-man50785.2021.9515479

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Such an encoder-decoder architecture is widely used to synthesize pixel-wise grasps [11,28,116,239,255]. Another similar formulation for pixel-wise grasp synthesis is called grasp manifolds, proposed by [88]. Since grasp map is more informative and could provide a global grasp affordance which indicates the grasp quality of the current viewpoints, it enables selection of the best view [116,239], provided the assumption that the camera is not fixed, which holds in most cases for robots.…”

Section: Pixel-level Grasp Map Synthesismentioning

confidence: 99%

Robotic Grasping from Classical to Modern: A Survey

Zhang¹,

Tang²,

Sun³

et al. 2022

Preprint

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Robotic Grasping has always been an active topic in robotics since grasping is one of the fundamental but most challenging skills of robots. It demands the coordination of robotic perception, planning, and control for robustness and intelligence. However, current solutions are still far behind humans, especially when confronting unstructured scenarios.In this paper, we survey the advances of robotic grasping, starting from the classical formulations and solutions to the modern ones. By reviewing the history of robotic grasping, we want to provide a complete view of this community, and perhaps inspire the combination and fusion of different ideas, which we think would be helpful to touch and explore the essence of robotic grasping problems. In detail, we firstly give an overview of the analytic methods for robotic grasping. After that, we provide a discussion on the recent state-ofthe-art data-driven grasping approaches rising in recent years. With the development of computer vision, semantic grasping is being widely investigated and can be the basis of intelligent manipulation and skill learning for autonomous robotic systems in the future.Therefore, in our survey, we also briefly review the recent progress in this topic. Finally, we discuss the open problems and the future research directions that may be important for the human-level robustness, autonomy, and intelligence of robots.

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Section: Pixel-level Grasp Map Synthesismentioning

confidence: 99%

Robotic Grasping from Classical to Modern: A Survey

Zhang¹,

Tang²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Contrary to methods classifying grasps, generative models can be trained to generate grasp poses from data [6] but might require additional sample refinement. While the generator in [40] considers possible collisions in the scene, [56] proposes to learn a grasp distribution over the object's manifold. [29] uses scene representation learning to learn grasp qualities and explicitly predict 3D rotations.…”

Section: Grasp and Motion Optimization On Real Robotsmentioning

confidence: 99%