Extraction of Physically Plausible Support Relations to Predict and Validate Manipulation Action Effects

Kartmann, Rainer; Paus, Fabian; Grotz, Markus; Asfour, Tamim

doi:10.1109/lra.2018.2859448

Cited by 30 publications

(14 citation statements)

References 19 publications

(27 reference statements)

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“…This method pushes down and spreads out cluttered objects to rearrange them, which is not suitable for fragile objects. In [11,28], Markus et al propose graph-based semantic perception and physically plausible support relations extraction method. However, these methods only give the relationship between different objects, and do not provide the overall order planning of object grasping.…”

Section: Grasp Strategymentioning

confidence: 99%

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

et al. 2022

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In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning. Firstly, a novel stacked object classification network (SOCN) is proposed to realize stacked object recognition. The network takes into account the visible volume of the objects to further adjust its inverse density parameters, which makes the training process faster and smoother. At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning. Subsequently, a grasping order planning method is investigated, which depends on the security score and extracts the geometric relations and dependencies between stacked objects, it calculates the security score based on object relation, classification, and size. The proposed method is evaluated by using a depth camera and a UR-10 robot to complete grasping tasks. The results show that our method has high accuracy for stacked object classification, and the grasping order effectively and successfully executes safely.

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Section: Grasp Strategymentioning

confidence: 99%

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

et al. 2022

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“…A safe manipulation strategy based on the spatial relationship between objects in the uncertain scenario has been proposed in ref. [133], in which its purpose is to avoid falling objects during grasping. The existing CNN-based techniques for objects grasping did not consider manipulation relationship between the objects on top of each other.…”

Section: A Physical Support Relations Between Objectsmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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SUMMARY Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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“…This expression is reasonable for scene understanding and provides research foundation for robot operation planning. Kartmann [ 29 ] infers physically reasonable support relations between objects without any prior knowledge about the physical properties (mass distribution and friction coefficient). By the virtual force analysis, the uncertainty of the support relations is taken into account in the prediction.…”

Section: Related Workmentioning

confidence: 99%

Spatial Topological Relation Analysis for Cluttered Scenes

Zhang

et al. 2020

Sensors

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The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this paper, we presented the classification of spatial topological relations by dividing the intersection space into six parts. In order to improve accuracy and reduce computing time, convex hulls are utilized to represent the boundary of objects and the spatial topological relations can be determined by the category of points in point clouds. We verified our method on the datasets. The result demonstrated that we have great improvement comparing with the previous method.

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Extraction of Physically Plausible Support Relations to Predict and Validate Manipulation Action Effects

Cited by 30 publications

References 19 publications

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Spatial Topological Relation Analysis for Cluttered Scenes

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