While we have traditionally understood the hippocampus to be involved in memory and navigation, it also appears that it has a role in language processing, creation and prediction. An obvious explanation for this is that language is impossible if linguistic signs cannot be remembered and retrieved. Because linguistic signs are definitionally biologically neutral or arbitrary, we must use the brain's apparatus for learning and storing information from the external world to store and retrieve them. Although plausible, this explanation fails to take into account the hippocampus' role in navigation as a contributing element in the processing, storage and retrieval of linguistic signs. Because the hippocampus also represents non-physical spaces through the same basic cognitive mechanisms with which it represents physical space, it is possible that the semantic content of linguistic signs is encoded in a fundamentally similar way to how navigational information is encoded. If true, this could have implications for education in general, and second language acquisition specifically. These experiments test whether there might be a learning benefit to presenting information in consistent spatial locations by having participants learn word associations in a 3-dimensional virtual environment. The experiments found that this was not the case. These findings have implications for education. Some educational paradigms stress learning in relevant contexts. These results suggest that physical location may not be an important component of a learning environment.

<div>We describe a new task called The Amazing Race: Robot Edition. In this task, the robot is placed in a real, unknown environment, without a map, and asked to find a designated location. It will need to explore its surroundings, find and approach people, engage them in a dialogue to obtain directions to the goal, and follow those directions to the hallway with the goal. We describe and implement a variety of robotic behaviors that performs each of these functions. We test these in the real world in test environments that were distinct from the training environments where we developed our methods and trained our models. Additionally, these test environments were completely unmodified and reflect the state of the real world.</div><div>First, we describe how our robotic system solves this problem where the environment is constrained to a single floor or a single building. We demonstrate that we are able to find a goal location in never-before-seen environments. Next, we describe a machine-learned approach to the dialogue and components of our system to make it more robust to the diversity and noisiness of navigational instructions someone may provide.</div>

The use of Unmanned Aerial Vehicles (UAVs) are rapidly expanding and taking on new roles in the military. In the area of training and targeting vehicles, control systems are expanding the functionality of UAVs beyond their initially designed purpose. Aeromech Engineering’s NXT UAV is a high speed target drone that is intended to simulate a small aircraft threat. However, in the interest of increasing functionality, enabling NXT to accomplish wings level skidding turns provides the basis for a UAV that can simulate a threat from a missile. Research was conducted to investigate the aerodynamic and performance characteristics of a winged vehicle performing high acceleration skidding turns. Initially, a linear model was developed using small disturbance theory. The model was further improved by developing a six degree of freedom simulation. A controller using four loop closures and utilizing both rudder and aileron for control was developed. Any outside guidance system that navigates using a heading command can easily be integrated into this controller design. Simulations show this controller enables the NXT UAV to accomplish up to 3 G wings level skidding turns. Further testing, showed that the controller was able to tolerate significant turbulence, sensor noise, loop failures and changes within the plant dynamics. This research shows how it is possible for a winged UAV to easily maneuver using wings level skid turns.

Despite the advances in antiretroviral therapy (ART) a disproportionate number of people living with HIV (PLWH) remain limited in their access and use of health care and treatment, including racial/ethnic minorities, persons with mental health and substance use disorders and persons experiencing homelessness or unstable housing. Patient navigation programs have emerged as a potential effective and efficient use of resources to reach and engage these vulnerable populations as part of the HIV service delivery system. This dissertation contains three chapters that aim to identify and assess the performance and mechanisms for navigation programs working with PLWH who experience homelessness and co-occurring substance use and mental health disorders. Study 1, Developing a Reliable and Valid Composite Measure of Effectiveness for HIV Navigation Programs for PLWH who are homeless/unstably housed, describes the development of a multidimensional outcome measure to assess the performance of navigation programs for this population. The composite measure was comprised of seven indicator variables: linkage to care, retention in care and adherence to treatment, patient experience of care, physical and mental health related quality of life and housing stability. Using multivariate analyses, a 3 item measure of retention, adherence, and housing stability was found to have high goodness of fit and strong predictive association with viral suppression. Study 2, Classifying Components of HIV Navigation Programs for PLWH who are homeless/unstably housed, used a latent-class analysis to identify common patterns of activities, modalities of communication, location of work, and staff composition among highest utilizers of services. Results showed that types of activities, work setting and modality of contact were significantly associated with increased retention in care. No difference in activity, staffing patterns, work setting or modality of communication of navigation programs were found on viral suppression rates. Study 3, An Economic Evaluation of HIV Navigation Programs Working with PLWH who are Homeless/Unstably housed assessed costs and net benefits of these navigation programs overall and in subpopulations. Cost utility and net benefit analyses performed indicated that navigation programs for PLWH who are homeless/unstably housed are a potential efficient investment of resources at various willingness-to-pay thresholds. Navigation programs provide a myriad of services for PLWH who are homeless/unstably housed and must be flexible in their approach to address the multiple medical and psychosocial needs of this population. The results of this dissertation provide information for improving the design, measuring performance and costs and benefits of navigation programs as part of the HIV service delivery system for PLWH who experience homelessness. / 2020-06-30T00:00:00Z

With the advent of the Global Position System (GPS), we now have the ability to determine absolute position anywhere on the globe. Although GPS systems work well in open environments with no overhead obstructions, they are subject to large unavoidable errors when the reception from some of the satellites is blocked. This occurs frequently in urban environments, such as downtown New York City. GPS systems require at least four satellites visible to maintain a good position 'fix' . Tall buildings and tunnels often block several, if not all, of the satellites. Additionally, due to Selective Availability (SA), where small amounts of error are intentionally introduced, GPS errors can typically range up to 100 ft or more. This thesis proposes several methods for improving the position estimation capabilities of a system by incorporating other sensor and data technologies, including Kalman filtered inertial navigation systems, rule-based and fuzzy-based sensor fusion techniques, and a unique map-matching algorithm. / Master of Science

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Machine learning developments have contributed to overcome a wide range of issues, including robotic motion, autonomous navigation, and natural language processing. Of note are the advancements of reinforcement learning in the area of object goal navigation — the task of autonomously traveling to target objects with minimal a priori knowledge of the environment. Given the sparse placement of goals in unknown scenes, exploration is essential for reaching remote objects of interest that are not immediately visible to autonomous agents. Sparse rewards are a crucial problem in reinforcement learning that arises in object goal navigation, as positive rewards are only attained when targets are found at the end of an agent’s trajectory. As such, this work explores object goal navigation and the challenges it presents, along with the relevant reinforcement learning techniques applied to the task. An ablation study of the baseline approach for the RoboTHOR 2021 object goal navigation challenge is presented and used to guide the development of an on-policy agent that is computationally less expensive and obtains greater success in unseen environments. Then, original object goal navigation reward schemes that aggregate episodic and long-term novelty bonuses are proposed, and obtain success rates comparable to the respective object goal navigation benchmark at a fraction of training interactions with the environment.