Results 31 to 40 of about 141,405 (364)
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2019 In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene. We propose a novel Generative Residual Convolutional Neural Network (
Sulabh Kumra, Shirin Joshi, F. Sahin
semanticscholar +1 more source
CoRR, 2022 Transparent objects are common in day-to-day life and hence find many applications that require robot grasping. Many solutions toward object grasping exist for non-transparent objects. However, due to the unique visual properties of transparent objects, standard 3D sensors produce noisy or distorted measurements.
Hrishikesh Gupta +3 more
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GR-ConvNet v2: A Real-Time Multi-Grasp Detection Network for Robotic Grasping
Sensors, 2022 We propose a dual-module robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene.
Sulabh Kumra, Shirin Joshi, Ferat Sahin
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Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2018 Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help ...
Andy Zeng +5 more
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DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2023 Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep re-inforcement learning (DRL) based methods leverage human demonstrations to reduce sample ...
Qingtao Liu +7 more
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Atoms, 2022 Relativistic atomic and molecular structure calculations start from very different premises to quantum electrodynamics (QED). QED predicts radiative corrections that can nly be approximated crudely in today's atomic structure programs. This paper describes rigorous simple algorithms for including accurate vacuum polarization and self-energy corrections
Ian Grant, Harry Quiney
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Nut Unfastening by Robotic Surface Exploration
Robotics, 2021 In this paper, we present a novel concept and primary investigations regarding automated unfastening of hexagonal nuts by means of surface exploration with a compliant robot.
Alireza Rastegarpanah +3 more
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Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach [PDF]
Robotics: Science and Systems, 2018 This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-
D. Morrison, Peter Corke, J. Leitner
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Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping [PDF]
Neural Information Processing Systems, 2023 The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance.
Tianhao Wu +4 more
semanticscholar +1 more source
Stochastic Models and Control of Anchoring Mechanisms for Grasping in Microgravity
Applied Sciences, 2022 Robots equipped with anchoring mechanisms have attractive applications in asteroid exploration. However, complex application scenarios bring great challenges to the modeling and control of anchoring mechanisms.
Qingpeng Wen, Jun He, Feng Gao
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