Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018

HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such 'soft data' remains challenging. Here, the Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams to address these gaps by formally combining the benefits of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments by sketching and labeling arbitrary landmarks across the environment. Dynamic updating of the environment model while during search allows robotic agents to actively query humans for novel and relevant semantic data, thereby improving beliefs of unknown environments and states for improved online planning. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates required for interception versus conventional planning based solely on robotic sensing. Human subject studies in the same environment (n = 36) demonstrate an average doubling in dynamic target capture rate compared to the lone robot case, and highlight the robustness of active probabilistic reasoning and semantic sensing over a range of user characteristics and interaction modalities

Acting on non-communicable diseases in low- and middle-income tropical countries

The classical portrayal of poor health in tropical countries is one of infections and parasites, contrasting with wealthy western countries, where unhealthy diet and behaviours cause non-communicable diseases (NCDs) like heart disease and cancer. Using international mortality data, we show that most NCDs cause more deaths at any age in low- and middle-income tropical countries than in high-income western countries. Causes of NCDs in low- and middle-income countries include poor nutrition and living environment, infections, insufficient regulation of tobacco and alcohol, and under-resourced and inaccessible healthcare. We identify a comprehensive set of actions across health, social, economic and environmental sectors that can confront NCDs in low- and middle-income tropical countries and reduce global health inequalities

Prenatal sex hormone effects on child and adult sex-typed behavior: methods and findings

There is now good evidence that human sex-typed behavior is influenced by sex hormones that are present during prenatal development, confirming studies in other mammalian species. Most of the evidence comes from clinical populations, in which prenatal hormone exposure is atypical for a person's sex, but there is increasing evidence from the normal population for the importance of prenatal hormones. In this paper, we briefly review the evidence, focusing attention on the methods used to study behavioral effects of prenatal hormones. We discuss the promises and pitfalls of various types of studies, including those using clinical populations (concentrating on those most commonly studied, congenital adrenal hyperplasia, androgen insensitivity syndrome, ablatio penis, and cloacal exstrophy), direct measures of hormones in the general population (assayed through umbilical cord blood, amniotic fluid, and maternal serum during pregnancy), and indirect measures of hormones in the general population (inferred from intrauterine position and biomarkers such as otoacoustic emissions, finger length ratios, and dermatoglyphic asymmetries). We conclude with suggestions for interpreting and conducting studies of the behavioral effects of prenatal hormones. © 2004 Elsevier Ltd. All rights reserved