Category-based task specific grasping

Nikandrova, Ekaterina; Kyrki, Ville

doi:10.1016/j.robot.2015.04.002

Cited by 31 publications

(20 citation statements)

References 32 publications

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“…Nikandrova et al [32] demonstrated a category-based approach for object grasping. Different methods have been proposed to determine the object categories automatically [26], [30] and define an object representation and a similarity metric for grasp transfer [5].…”

Section: Related Workmentioning

confidence: 99%

Transferring Grasp Configurations using Active Learning and Local Replanning

Tian

Wang

Manocha

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We present a new approach to transfer grasp configurations from prior example objects to novel objects. We assume the novel and example objects have the same topology and similar shapes. We perform 3D segmentation on these objects using geometric and semantic shape characteristics. We compute a grasp space for each part of the example object using active learning. We build bijective contact mapping between these model parts and compute the corresponding grasps for novel objects. Finally, we assemble the individual parts and use local replanning to adjust grasp configurations while maintaining its stability and physical constraints. Our approach is general, can handle all kind of objects represented using mesh or point cloud and a variety of robotic hands.

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Section: Related Workmentioning

confidence: 99%

Transferring Grasp Configurations using Active Learning and Local Replanning

Tian

Wang

Manocha

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…The simulation based method is also applied by E. Nikandrova and V. Kyrki [21] to find an optimal grasp. A probabilistic approach for task-specific stable grasping of objects with shape variations inside the category is presented in the work.…”

Section: Quantitative Evaluation Of Gripper Designsmentioning

confidence: 99%

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Wolniakowski

Miatliuk

Gosiewski

et al. 2017

J Intell Robot Syst

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In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both selected gripper mechanism parameters, and parameters of the finger geometry. We suggest six gripper quality indices that indicate different aspects of the performance of a gripper given a CAD model of an object and a task description. These quality indices are then used to learn task-specific finger designs based on dynamic simulation. We demonstrate our gripper optimization on a parallel finger type gripper described by twelve parameters. We furthermore present a parametrization of the grasping task and context, which is essential as an input to the computation of gripper performance. We exemplify important aspects of the indices by looking at their performance on subsets of the parameter space by discussing the decoupling of parameters and show optimization results for two use cases for different task contexts. We provide a qualitative evaluation of the obtained results based on existing design guidelines and our engineering experience. In addition, we show that with our method we achieve superior alignment properties compared to a naive approach with a cutout based on the "inverse of an object". Furthermore, we provide an experimental evaluation of our proposed method by verifying the simulated grasp outcomes through a real-world experiment.

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“…Simulation as a tool in the context of grasping and gripper design is mostly encountered in problems of optimal grasp planning and structural design [11], [12]. In our previous research, we have used dynamic simulation to obtain feedback in the finger geometry design phase [3], [13].…”

Section: B Gripper Learning In Simulationmentioning

confidence: 99%

Optimizing grippers for compensating pose uncertainties by dynamic simulation

Wolniakowski

Kramberger

Gams

et al. 2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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Gripper design process is one of the interesting challenges in the context of grasping within industry. Typically, simple parallel-finger grippers, which are easy to install and maintain, are used in platforms for robotic grasping. The context switches in these platforms require frequent exchange of gripper fingers to accommodate grasping of new products, while subjected to numerous constraints, such as workcell uncertainties due to the vision systems used. The design of these fingers consumes the man-hours of experienced engineers, and involves a lot of trial-and-error testing. In our previous work, we have presented a method to automatically compute the optimal finger shapes for defined task contexts in simulation. In this paper, we show the performance of our method in an industrial grasping scenario. We first analyze the uncertainties of the used vision system, which are the major source of grasping error. Then, we perform the experiments, both in simulation and in a real setting. The experiments confirmed the validity of our approach. The computed finger design was employed in a real industrial assembly scenario.

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Category-based task specific grasping

Cited by 31 publications

References 32 publications

Transferring Grasp Configurations using Active Learning and Local Replanning

Transferring Grasp Configurations using Active Learning and Local Replanning

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Optimizing grippers for compensating pose uncertainties by dynamic simulation

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