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

Context-aware 6D Pose Estimation of Known Objects using RGB-D data

6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navigation etc, require the correct pose of objects present in a scene to perform their specific task. It becomes even harder when the objects are placed in a cluttered scene and the level of occlusion is high. Prior works have tried to overcome this problem but could not achieve accuracy that can be considered reliable in real-world applications. In this paper, we present an architecture that, unlike prior work, is context-aware. It utilizes the context information available to us about the objects. Our proposed architecture treats the objects separately according to their types i.e; symmetric and non-symmetric. A deeper estimator and refiner network pair is used for non-symmetric objects as compared to symmetric due to their intrinsic differences. Our experiments show an enhancement in the accuracy of about 3.2% over the LineMOD dataset, which is considered a benchmark for pose estimation in the occluded and cluttered scenes, against the prior state-of-the-art DenseFusion. Our results also show that the inference time we got is sufficient for real-time usage

The temperature dependence of kinesin motor-protein mechanochemistry

Biophysical studies of the mechanochemical cycle of kinesin motors are essential for understanding the mechanism of energy conversion. Here, we report a systematic study of the impact of temperature on velocity and run length of homodimeric Drosophila kinesin-1, homodimeric C. elegans OSM-3 and heterodimeric C. elegans kinesin-II motor proteins using in vitro single-molecule motility assays. Under saturated ATP conditions, kinesin-1 and OSM-3 are fast and processive motors compared to kinesin-II. From in vitro motility assays employing single-molecule fluorescence microscopy, we extracted single-motor velocities and run lengths in a temperature range from 15 °C to 35 °C. Both parameters showed a non-Arrhenius temperature dependence for all three motors, which could be quantitatively modeled using a simplified, two-state kinetic model of the mechanochemistry of the three motors, providing new insights in the temperature dependence of their mechanochemistry

Glutathione S-Transferase Gene Polymorphisms and Treatment Outcome in Cervical Cancer Patients under Concomitant Chemoradiation.

PurposeCisplatin based concomitant chemoradiation (CRT) is the standard treatment for locally advanced cervical cancer (CC). Glutathione S-transferase (GST), a phase II antioxidant enzyme is induced by oxidative stress generated by drugs and reactive oxidants. The present study was undertaken to evaluate the association of GSTM1, T1 and P1 polymorphisms with the outcome of CRT treatment in CC patients.MethodsA total of 227 cervical cancer patients with stages IIB-IIIB treated with the same chemoradiotherapy regimen were enrolled and genotyped for GSTM1, T1 and P1 gene polymorphisms by multiplex polymerase chain reaction (mPCR) and PCR-restriction fragment length polymorphism (PCR-RFLP). Overall survival was evaluated using Kaplan-Meier survival function and Cox proportional hazards model. All data were analyzed using SPSS (version 21.0).ResultsStratified analysis showed that GSTM1 null (M1-) genotype was associated with a significantly better survival among patients with stage IIB cervical cancer (log-rank P = 0.004) than cases with stage IIIA/IIIB. Death and recurrence were significantly higher in patients with GSTM1 present genotype (M1+) (P = 0.037 and P = 0.003 respectively) and those with M1- showed reduced hazard of death with an adjusted hazard ratio 'HR' of 0.47 (95% CI, 0.269-0.802, P = 0.006). Women with M1- genotype as well as in combination with GSTT1 null (T1-), GSTP1 (AG+GG) and GSTT1 null/GSTP1 (AG+GG) showed better survival and also reduced risk of death (HR = 0.31, P = 0.016; HR = 0.45, P = 0.013; HR = 0.31, P = 0.02 respectively).ConclusionsTo the best of our knowledge, this is the first study to correlate the association of GSTM1, T1 and P1 gene polymorphisms with treatment outcome of CRT treated CC patients. Our results suggested that individuals with GSTM1 null genotype and in combination with GSTT1 null and GSTP1 (AG+GG) had a survival advantage. Such genetic studies may provide prognostic information in CRT treated CC patients

An automated in vitro motility assay for high-throughput studies of molecular motors

Molecular motors, essential to force-generation and cargo transport within cells, are invaluable tools for powering nanobiotechnological lab-on-a-chip devices. These devices are based on in vitro motility assays that reconstitute molecular transport with purified motor proteins, requiring a deep understanding of the biophysical properties of motor proteins and thorough optimization to enable motility under varying environmental conditions. Until now, these assays have been prepared manually, severely limiting throughput. To overcome this limitation, we developed an in vitro motility assay where sample preparation, imaging and data evaluation are fully automated, enabling the processing of a 384-well plate within less than three hours. We demonstrate the automated assay for the analysis of peptide inhibitors for kinesin-1 at a wide range of concentrations, revealing that the IAK domain responsible for kinesin-1 auto-inhibition is both necessary and sufficient to decrease the affinity of the motor protein for microtubules, an aspect that was hidden in previous experiments due to scarcity of data.The code used for automated data analysis can be found here: https://github.com/thawn/AutoTipTrac

Kaplan-Meier function for overall survival among women treated for cervical cancer, by <i>GSTM1</i>, <i>T1</i> and <i>P1</i> genotypes in combination.

<p>Survival difference by log-rank test.</p

Kaplan-Meier function for overall survival among women treated for cervical cancer, by <i>GSTM1</i>, <i>T1</i> and <i>P1</i> genotypes.

<p>Survival difference by log-rank test.</p