Explicit Forgetting Algorithms for Memory Based Learning

Memory-based learning algorithms lack a mechanism for tracking time-varying associative mappings. To widen their applicability, they must incorporate explicit forgetting algorithms to selectively delete observations. We describe Time-Weighted, Locally-Weighted and Performance-Error Weighted forgetting algorithms. These were evaluated with a Nearest-Neighbor Learner in a simple classification task. Locally-Weighted Forgetting outperformed Time-Weighted Forgetting under time-varying sampling distributions and mappings, and did equally well when only the mapping varied. Performance-Error forgetting tracked about as well as the other algorithms, but was superior since it permitted the Nearest-Neighbor learner to approach the Bayes\u27 misclassification rate when the input-output mapping became stationary

A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces

Displays for telemanipulation

Visual displays drive the human operator's highest bandwidth sensory input channel. Thus, no telemanipulation system is adequate which does not make extensive use of visual displays. Although an important use of visual displays is the presentation of a televised image of the work scene, visual displays are examined for presentation of nonvisual information (forces and torques) for simulation and planning, and for management and control of the large numbers of subsystems which make up a modern telemanipulation system

Maternity Care and Consumer-Driven Health Plans

Compares out-of-pocket costs of maternity care under consumer-driven health plans (CDHP) to a traditional health insurance plan. Explores related factors including prenatal care coverage and unpredictability of costs for delivery and hospital stays

Robotic Sensorimotor Learning in Continuous Domains

We propose that some aspects of task based learning in robotics can be approached using nativist and constructivist views on human sensorimotor development as a metaphor. We use findings in developmental psychology, neurophysiology, and machine perception to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a dexterous three fingered hand, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up some important issues such as observation sparsity in high dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for generalization directions determining projections of high dimensional data sets which capture task invariants. Additionally, the learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate grave mistakes during learning and not damage itself. We address this by the use of an instrumented compliant robot wrist which controls impact forces

Sensorimotor Learning Using Active Perception in Continuous Domains

We propose that some aspects of task based learning in robotics can be approached using nativist and constructivist views on human sensorimotor development as a metaphor. We use findings in developmental psychology, neurophysiology, and machine perception to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a dexterous three fingered hand, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be though of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up some important issues such as observation sparsity in high dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for generalization directions determining projections of high dimensional data sets which capture task invariants. Additionally, the learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate grave mistakes during learning and not damage itself. We address this by the use of an instrumented compliant robot wrist which controls impact forces

Putting Women's Health Care Disparities on the Map: Examining Racial and Ethnic Disparities at the State Level

Assesses the racial/ethnic disparities in women's health status, access to and utilization of health care, and social factors such as poverty and gender wage gap by state. Examines how healthcare payment and worker shortages affect access to care

Learning for Coordination of Vision and Action

We define the problem of visuomotor coordination and identify bottleneck problems in the implementation of general purpose vision and action systems. We conjecture that machine learning methods provide a general purpose mechanism for combining specific visual and action modules in a task-independent way. We also maintain that successful learning systems reflect realities of the environment, exploit context information, and identify limitations in perceptual algorithms which cannot be captured by the designer. We then propose a multi-step find-and-fetch mobile robot search and retrieval task. This task illustrates where current learning approaches provide solutions and where future research opportunities exist

Health and Access to Care and Coverage for Lesbian, Gay, Bisexual, and Transgender Individuals in the U.S.

Lesbian, gay, bisexual, and transgender (LGBT) individuals often face challenges and barriers to accessing needed health services and, as a result, can experience worse health outcomes. These challenges can include stigma, discrimination, violence, and rejection by families and communities, as well as other barriers, such as inequality in the workplace and health insurance sectors, the provision of substandard care, and outright denial of care because of an individual's sexual orientation or gender identity. While sexual and gender minorities have many of the same health concerns as the general population, they experience certain health challenges at higher rates, and also face several unique health challenges. In particular, research suggests that some subgroups of the LGBT community have more chronic conditions as well as higher prevalence and earlier onset of disabilities than heterosexuals. Other major health concerns include HIV/AIDS, mental illness, substance use, and sexual and physical violence. In addition to the higher rates of illness and health challenges, some LGBT individuals are more likely to experience challenges obtaining care. Barriers include gaps in coverage, cost-related hurdles, and poor treatment from health care providers. Several recent changes within the legal and policy landscape serve to increase access to care and insurance for LGBT individuals and their families. Most notably the implementation of the Affordable Care Act (ACA) and the Supreme Court's overturning of a major portion of the Defense of Marriage Act (DOMA), as well as recent steps taken by the Obama Administration to promote equal treatment of LGBT people and same-sex couples in the nation's health care system have reshaped policy affecting LGBT individuals and their families. The ACA expands access to health insurance coverage for millions, including LGBT individuals, and includes specific protections related to sexual orientation and gender identity. The Supreme Court ruling on DOMA resulted in federal recognition of same-sex marriages for the first time, which also serves to provide new health insurance coverage options. In addition, President Obama's administration has undertaken a variety of other initiatives to improve the health and well-being of LGBT individuals, families, and communities. This issue brief provides an overview of what is known about LGBT health status, coverage, and access in the United States, and reviews the implications of the ACA, the overturning of DOMA, and other recent policy developments for LGBT individuals and their families going forward

A Vision-Based Learning Method for Pushing Manipulation

We describe an unsupervised on-line method for learning of manipulative actions that allows a robot to push an object connected to it with a rotational point contact to a desired point in image-space. By observing the results of its actions on the object\u27s orientation in image-space, the system forms a predictive forward empirical model. This acquired model is used on-line for manipulation planning and control as it improves. Rather than explicitly inverting the forward model to achieve trajectory control, a stochastic action selection technique [Moore, 1990] is used to select the most informative and promising actions, thereby integrating active perception and learning by combining on-line improvement, task-directed exploration, and model exploitation. Simulation and experimental results of the approach are presented