Pushing and grasping for autonomous learning of object models with foveated vision

Bevec, Robert; Ude, Aleš

doi:10.1109/icar.2015.7251462

Cited by 3 publications

(6 citation statements)

References 31 publications

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“…The grasping module can process this information arbitrarily. However, for purposes of illustration and evaluation, we provide the grasping data in a similar form to that defined in [ 29 ]. A grasp is planned by calculating the mean position

and the dominant principal axis

of the object.…”

Section: Methodsmentioning

confidence: 99%

“…The main advantage of this approach is that it provides a guess of the surface of the object and also offers a measurement of uncertainty of the shape, which can be used to decide where to further inspect the object. It has also been shown that when dealing with novel objects, a reactive grasping mechanism can be used to grasp objects using a humanoid robot by determining its dominant axis and centroid [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors

Soria

Bevec

Arrue

et al. 2016

Sensors

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Giving unmanned aerial vehicles (UAVs) the possibility to manipulate objects vastly extends the range of possible applications. This applies to rotary wing UAVs in particular, where their capability of hovering enables a suitable position for in-flight manipulation. Their manipulation skills must be suitable for primarily natural, partially known environments, where UAVs mostly operate. We have developed an on-board object extraction method that calculates information necessary for autonomous grasping of objects, without the need to provide the model of the object’s shape. A local map of the work-zone is generated using depth information, where object candidates are extracted by detecting areas different to our floor model. Their image projections are then evaluated using support vector machine (SVM) classification to recognize specific objects or reject bad candidates. Our method builds a sparse cloud representation of each object and calculates the object’s centroid and the dominant axis. This information is then passed to a grasping module. Our method works under the assumption that objects are static and not clustered, have visual features and the floor shape of the work-zone area is known. We used low cost cameras for creating depth information that cause noisy point clouds, but our method has proved robust enough to process this data and return accurate results.

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and the dominant principal axis

of the object.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors

Soria

Bevec

Arrue

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another approach, the robot may grasp the object directly and show it to the camera in different poses [8,9]. Finally, the object can be rotated in place by pushing it with the gripper of the robot, while a stationary sensor generates the model [10].…”

Section: State Of the Artmentioning

confidence: 99%

“…Some of the techniques based on the robot manipulating the object have strategies to pick up the workpiece by themselves and solve the first requirement regarding the full automation. This can be done if the robot is able to pick objects up by itself [8] and extract them from cluttered scenes [10]. However, approaches from this group still share the drawback, that the object models are not complete since either the bottom side of the object is missing [7,10] or some parts of the workpiece are occluded by the gripper of the robot [8].…”

Section: State Of the Artmentioning

confidence: 99%

“…This can be done if the robot is able to pick objects up by itself [8] and extract them from cluttered scenes [10]. However, approaches from this group still share the drawback, that the object models are not complete since either the bottom side of the object is missing [7,10] or some parts of the workpiece are occluded by the gripper of the robot [8]. Approaches exist to handle this occlusion by detecting the gripper, meshing the models and performing hole closing algorithms on the meshes [9].…”

Section: State Of the Artmentioning

confidence: 99%

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Robot-Based Creation of Complete 3D Workpiece Models

Rohner

Henrich

2022

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

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A complete object database containing a model (representing geometric and texture information) of every possible workpiece is a common necessity e.g. for different object recognition or task planning approaches. The generation of these models is often a tedious process. In this paper we present a fully automated approach to tackle this problem by generating complete workpiece models using a robotic manipulator. A workpiece is recorded by a depth sensor from multiple views for one side, then turned, and captured from the other side. The resulting point clouds are merged into one complete model. Additionally, we represent the information provided by the object’s texture using keypoints. We present a proof of concept and evaluate the precision of the final models. In the end we conclude the usefulness of our approach showing a precision of around 1 mm for the resulting models.

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Kinematically-Informed Interactive Perception: Robot-Generated 3D Models for Classification

Griffin¹,

Corso²

2019

Preprint

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To be useful in everyday environments, robots must be able to observe and learn about objects. Recent datasets enable progress for classifying data into known object categories; however, it is unclear how to collect reliable object data when operating in cluttered, partially-observable environments. In this paper, we address the problem of building complete 3D models for real-world objects using a robot platform, which can remove objects from clutter for better classification. Furthermore, we are able to learn entirely new object categories as they are encountered, enabling the robot to classify previously unidentifiable objects during future interactions. We build models of grasped objects using simultaneous manipulation and observation, and we guide the processing of visual data using a kinematic description of the robot to combine observations from different viewpoints and remove background noise. To test our framework, we use a mobile manipulation robot equipped with an RGBD camera to build voxelized representations of unknown objects and then classify them into new categories. We then have the robot remove objects from clutter to manipulate, observe, and classify them in real-time.

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Pushing and grasping for autonomous learning of object models with foveated vision

Cited by 3 publications

References 31 publications

Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors

Extracting Objects for Aerial Manipulation on UAVs Using Low Cost Stereo Sensors

Robot-Based Creation of Complete 3D Workpiece Models

Kinematically-Informed Interactive Perception: Robot-Generated 3D Models for Classification

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