Review of deep learning methods in robotic grasp detection

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed

Robotic grasp pose detection using deep learning

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. This paper proposes the use of a transfer learning technique with deep convolutional neural networks to learn how to visually identify the grasping configurations for a parallel plate gripper that will be used to grasp various household objects. The Red-Green-Blue-Depth (RGB-D) data from the Cornell Grasp Dataset is used to train the network model using an end-to-end learning method. With this method, we achieve a grasping configuration prediction accuracy of 93.91%

Review of Deep Learning Methods in Robotic Grasp Detection

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed

The Young Men’s Survey Phase II: Hepatitis B Immunization and Infection Among Young Men Who Have Sex With Men

Objectives. We measured the prevalence of hepatitis B virus (HBV) immunization and HBV infection among men aged 23 to 29 years who have sex with men

Associations of Race/Ethnicity With HIV Prevalence and HIV-Related Behaviors Among Young Men Who Have Sex With Men in 7 Urban Centers in the United States

Abstract: Using data from a multisite venue-based survey of male subjects aged 15 to 22 years, we examined racial/ethnic differences in demographics, partner type, partner type-specific condom use, drug use, and HIV prevalence in 3316 US black, multiethnic black, Latino, and white men who have sex with men (MSM). We further estimated associations of these factors with HIV infection and their influence on racial/ethnic disparities in HIV prevalence. HIV prevalences were 16% for both black and multiethnic black participants, 6.9% for Latinos, and 3.3% for whites. Paradoxically, potentially risky sex and drug-using behaviors were generally reported most frequently by whites and least frequently by blacks. In a multiple logistic regression analysis, positive associations with HIV included older age, being out of school or work, sex while on crack cocaine, and anal sex with another male regardless of reported condom use level. Differences in these factors did not explain the racial/ethnic disparities in HIV prevalence, with both groups of blacks experiencing more than 9 times and Latinos experiencing approximately twice the fully adjusted odds of infection compared with whites. Understanding racial/ethnic disparities in HIV risk requires information beyond the traditional risk behavior and partnership type distinctions. Prevention programs should address risks in steady partnerships, target young men before sexual initiation with male partners, and tailor interventions to men of color and of lower socioeconomic status

HIV Prevention Services Received at Health Care and HIV Test Providers by Young Men who Have Sex with Men: An Examination of Racial Disparities

We investigated whether there were racial/ethnic differences among young men who have sex with men (MSM) in their use of, perceived importance of, receipt of, and satisfaction with HIV prevention services received at health care providers (HCP) and HIV test providers (HTP) that explain racial disparities in HIV prevalence. Young men, aged 23 to 29 years, were interviewed and tested for HIV at randomly sampled MSM-identified venues in six U.S. cities from 1998 through 2000. Analyses were restricted to five U.S. cities that enrolled 50 or more black or Hispanic MSM. Among the 2,424 MSM enrolled, 1,522 (63%) reported using a HCP, and 1,268 (52%) reported having had an HIV test in the year prior to our interview. No racial/ethnic differences were found in using a HCP or testing for HIV. Compared with white MSM, black and Hispanic MSM were more likely to believe that HIV prevention services are important [respectively, AOR, 95% confidence interval (CI): 3.0, 1.97 to 4.51 and AOR, 95% CI: 2.7, 1.89 to 3.79], and were more likely to receive prevention services at their HCP (AOR, 95% CI: 2.5, 1.72 to 3.71 and AOR, 95% CI: 1.7, 1.18 to 2.41) and as likely to receive counseling services at their HTP. Blacks were more likely to be satisfied with the prevention services received at their HCP (AOR, 95% CI: 1.7, 1.14 to 2.65). Compared to white MSM, black and Hispanic MSM had equal or greater use of, perceived importance of, receipt of, and satisfaction with HIV prevention services. Differential experience with HIV prevention services does not explain the higher HIV prevalence among black and Hispanic MSM