6,351 research outputs found
Vision-Based Intelligent Robot Grasping Using Sparse Neural Network
In the modern era of Deep Learning, network parameters play a vital role in
models efficiency but it has its own limitations like extensive computations
and memory requirements, which may not be suitable for real time intelligent
robot grasping tasks. Current research focuses on how the model efficiency can
be maintained by introducing sparsity but without compromising accuracy of the
model in the robot grasping domain. More specifically, in this research two
light-weighted neural networks have been introduced, namely Sparse-GRConvNet
and Sparse-GINNet, which leverage sparsity in the robotic grasping domain for
grasp pose generation by integrating the Edge-PopUp algorithm. This algorithm
facilitates the identification of the top K% of edges by considering their
respective score values. Both the Sparse-GRConvNet and Sparse-GINNet models are
designed to generate high-quality grasp poses in real-time at every pixel
location, enabling robots to effectively manipulate unfamiliar objects. We
extensively trained our models using two benchmark datasets: Cornell Grasping
Dataset (CGD) and Jacquard Grasping Dataset (JGD). Both Sparse-GRConvNet and
Sparse-GINNet models outperform the current state-of-the-art methods in terms
of performance, achieving an impressive accuracy of 97.75% with only 10% of the
weight of GR-ConvNet and 50% of the weight of GI-NNet, respectively, on CGD.
Additionally, Sparse-GRConvNet achieve an accuracy of 85.77% with 30% of the
weight of GR-ConvNet and Sparse-GINNet achieve an accuracy of 81.11% with 10%
of the weight of GI-NNet on JGD. To validate the performance of our proposed
models, we conducted extensive experiments using the Anukul (Baxter) hardware
cobot
Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach
Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning
Phenomenological implications of the existence of conjugate families on the V-A structure of weak interactions
We investigate the possibility of mixing occurring between the families and the conjugate families of SO(n) grand unified theories. Such a mixing alters the V-A structure of the usual charged weak currents. By comparing with the data on muon and pion decays, we set limits on the corresponding mixing angles. We consider the separate cases corresponding to the conjugate neutrinos being either light or heavy
Neutron Star Crust in Strong Magnetic Fields
We discuss the effects of strong magnetic fields through Landau quantization
of electrons on the structure and stability of nuclei in neutron star crust. In
strong magnetic fields, this leads to the enhancement of the electron number
density with respect to the zero field case. We obtain the sequence of
equilibrium nuclei of the outer crust in the presence of strong magnetic fields
adopting most recent versions of the experimental and theoretical nuclear mass
tables. For G, it is found that some new nuclei appear in the
sequence and some nuclei disappear from the sequence compared with the zero
field case.
Further we investigate the stability of nuclei in the inner crust in the
presence of strong magnetic fields using the Thomas-Fermi model. The
coexistence of two phases of nuclear matter - liquid and gas, is considered in
this case. The proton number density is significantly enhanced in strong
magnetic fields G through the charge neutrality. We find nuclei
with larger mass number in the presence of strong magnetic fields than those of
the zero field. These results might have important implications for the
transport properties of the crust in magnetars.Comment: 6 pages including 3 figures; based on the talk presented at INPC2010,
Vancouver; Submitted to the Proceedings of the conference in J. Phys. Conf.
Se
- …