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 $B\sim 10^{16}$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 $B\sim 10^{17}$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