Probabilistic Models of Object Geometry with Application to Grasping

Glover, Jared; Rus, Daniela; Roy, Nicholas

doi:10.1177/0278364909340332

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…Probabilistic approaches have also been studied for shape modelling and reconstruction. Probabilistic generative models of object geometry, trained with shape correspondences, have been used for inferring two-dimensional boundaries of partially occluded, deformable objects, and to recognize and retrieve complex objects from a pile for grasping tasks [24]. However, these methods do not directly extend to processing views of three dimensional objects which contain multiple articulations and self-occlusions.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Farias

Marturi

Bekiroglu

2021

IEEE Robot. Autom. Lett.

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This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.

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Section: Related Workmentioning

confidence: 99%

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Farias

Marturi

Bekiroglu

2021

IEEE Robot. Autom. Lett.

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“…Besides this, the generative model is also available. In research [ 33 ], a generative model for the object geometry was studied and could be extended to recognize the object’s shape in a cluttered image, with which the robot calculates the grasping point; the example is as shown in Figure 3 .…”

Section: Geometric-uncertain Objectsmentioning

confidence: 99%

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

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“…The ability to recognize and manipulate objects is important for mobile robots performing useful services in everyday environments. In recent years, various research groups have made substantial progress in recognition and manipulation of everyday objects (Ciocarlie et al, 2007;Berenson and Srinivasa, 2008;Saxena et al, 2008;Collet Romea et al, 2009;Glover et al, 2009;Lai and Fox, 2009;Rasolzadeh et al, 2009). While the developed techniques are often able to deal with noisy data and incomplete models, they still have limitations with respect to their usability in longterm robot deployments in realistic environments.…”

Section: Introductionmentioning

confidence: 99%

Manipulator and object tracking for in-hand 3D object modeling

Krainin

Henry

Ren

et al. 2011

The International Journal of Robotics Research

164

141

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Recognizing and manipulating objects is an important task for mobile robots performing useful services in everyday environments. While existing techniques for object recognition related to manipulation provide very good results even for noisy and incomplete data, they are typically trained using data generated in an offline process. As a result, they do not enable a robot to acquire new object models as it operates in an environment. In this paper we develop an approach to building 3D models of unknown objects based on a depth camera observing the robot's hand while moving an object. The approach integrates both shape and appearance information into an articulated Iterative Closest Point approach to track the robot's manipulator and the object. Objects are modeled by sets of surfels, which are small patches providing occlusion and appearance information. Experiments show that our approach provides very good 3D models even when the object is highly symmetric and lacks visual features and the manipulator motion is noisy. Autonomous object modeling represents a step toward improved semantic understanding, which will eventually enable robots to reason about their environments in terms of objects and their relations rather than through raw sensor data.

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Probabilistic Models of Object Geometry with Application to Grasping

Cited by 9 publications

References 34 publications

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Manipulator and object tracking for in-hand 3D object modeling

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