68 research outputs found
Video_2_Deep learning-based control framework for dynamic contact processes in humanoid grasping.MP4
Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.</p
Video_3_Deep learning-based control framework for dynamic contact processes in humanoid grasping.MP4
Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.</p
Video_1_Deep learning-based control framework for dynamic contact processes in humanoid grasping.MP4
Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.</p
HbA1c is Positively Associated with Serum Carcinoembryonic Antigen (CEA) in Patients with Diabetes: A Cross-Sectional Study
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High-Coverage H<sub>2</sub> Adsorption on the Reconstructed Cu<sub>2</sub>O(111) Surface
The
adsorption of H<sub>2</sub> on the Cu<sub>2</sub>O(111) surface
has been studied by spin-polarized density functional theory (DFT+U)
calculations and atomic thermodynamics. It has been found that there
exists reconstruction on a stoichiometric Cu<sub>2</sub>O(111) surface.
The probability distribution of the reconstructed Cu<sub>2</sub>O(111)
surfaces as a function of temperature has been analyzed using Boltzmann
statistics. It has been found that the molecular H<sub>2</sub> prefers
to adsorption on the uncoordinated Cu<sub>CUS</sub> atom at low coverages
(1/4 or 1/2 monolayer), while totally dissociative H<sub>2</sub> is
preferred on the reconstructed Cu<sub>2</sub>O(111) surface at higher
coverages (3/4 or 1 monolayer). For H<sub>2</sub> splitting on the
Cu<sub>2</sub>O(111) surface, homolytical dissociative adsorption
on two surface-uncoordinated Cu<sub>CUS</sub> atoms is preferred which
is a new mechanism for H<sub>2</sub> on metal oxides. More interesting
is that the surface reconstruction will be recovered for eight hydrogen
atoms binding on four uncoordinated Cu<sub>CUS</sub> and four uncoordinated
O<sub>CUS</sub> atoms at saturation coverage. It has been found that
the adsorbed H atoms will put out the lattice oxygen to the surface
at higher coverage (five and six H<sub>2</sub>), which agrees well
with the experimental findings. The phase diagrams of H<sub>2</sub> binding on ideal and reconstructed Cu<sub>2</sub>O(111) surfaces
were plotted and analyzed. In addition, we compared and analyzed the
adsorption mechanisms of H<sub>2</sub> splitting on different metal
oxides
Photocatalytic degradation of PCP with TiO<sub>2</sub>, graphene-TiO<sub>2</sub> and without catalyst under different pH values: (a) pH = 1; (b) pH = 4; (c) pH = 10; (d) pH = 13.
<p>Photocatalytic degradation of PCP with TiO<sub>2</sub>, graphene-TiO<sub>2</sub> and without catalyst under different pH values: (a) pH = 1; (b) pH = 4; (c) pH = 10; (d) pH = 13.</p
Comparation of photocatalytic rate constants (k) of PCP with different photocatalyst conditions under 3 kinds of UV irradiation.
<p>Comparation of photocatalytic rate constants (k) of PCP with different photocatalyst conditions under 3 kinds of UV irradiation.</p
Comparation of phenol production under different photocatalyst conditions after 2 hours UV irradiation and its percentage of initial PCP.
<p>Comparation of phenol production under different photocatalyst conditions after 2 hours UV irradiation and its percentage of initial PCP.</p
Schematic experimental system for photo degradation experiments.
<p>Schematic experimental system for photo degradation experiments.</p
Supplementary figure -Supplemental material for Hydrogen peroxide promotes the activation of preeclampsia peripheral T cells
<p>Supplemental material, Supplementary figure for Hydrogen peroxide promotes the activation of preeclampsia peripheral T cells by Jingzhu Lv, Xiaojie Zhang, Caizhi Wang, Hongtao Wang, Ting Wang and Zhongqing Qian in Innate Immunity</p
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