Robot Programming from Fish Demonstrations.

Fish are capable of learning complex relations found in their surroundings, and harnessing their knowledge may help to improve the autonomy and adaptability of robots. Here, we propose a novel learning from demonstration framework to generate fish-inspired robot control programs with as little human intervention as possible. The framework consists of six core modules: (1) task demonstration, (2) fish tracking, (3) analysis of fish trajectories, (4) acquisition of robot training data, (5) generating a perception-action controller, and (6) performance evaluation. We first describe these modules and highlight the key challenges pertaining to each one. We then present an artificial neural network for automatic fish tracking. The network detected fish successfully in 85% of the frames, and in these frames, its average pose estimation error was less than 0.04 body lengths. We finally demonstrate how the framework works through a case study focusing on a cue-based navigation task. Two low-level perception-action controllers were generated through the framework. Their performance was measured using two-dimensional particle simulations and compared against two benchmark controllers, which were programmed manually by a researcher. The fish-inspired controllers had excellent performance when the robot was started from the initial conditions used in fish demonstrations (>96% success rate), outperforming the benchmark controllers by at least 3%. One of them also had an excellent generalisation performance when the robot was started from random initial conditions covering a wider range of starting positions and heading angles (>98% success rate), again outperforming the benchmark controllers by 12%. The positive results highlight the utility of the framework as a research tool to form biological hypotheses on how fish navigate in complex environments and design better robot controllers on the basis of biological findings.

SARS-CoV-2: The Impact of Co-Infections with Particular Reference to Mycoplasma pneumonia-A Clinical Review.

Background: After its 2019 outbreak in Wuhan, scientists worldwide have been studying the epidemiology and clinical characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in children. Evidence indicates that children with SARS-CoV-2 infection are more likely to develop upper and lower respiratory tract infections in association with other infectious agents, such as Mycoplasma pneumoniae. Here, we conducted a systematic review of SARS-CoV-2 and Mycoplasma pneumoniae co-infection and their clinical course in children. Methods: We evaluated the published literature on SARS-CoV-2 by using the medical databases PubMed, Embase, Cochrane Library, Scopus, and Web of Science. In the searches, the Medical Subject Heading (MeSH) terms "SARS-CoV-2 and Mycoplasma pneumoniae" AND "co-infection SARS-CoV-2" were used. Studies describing co-infection with SARS-CoV-2 and Mycoplasma pneumoniae in children were included in the review. The study was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: According to the PRISMA guidelines, of the 38 identified studies, 14 were conducted in children (children/adolescents 0-18 years), 6 of which were included in this review. In total, 5867 children under the age of 17 years were diagnosed with SARS-CoV-2 infection through real-time polymerase chain reaction analysis of nasopharyngeal swabs to detect viral RNA. Elevated serum IgM levels specific to Mycoplasma pneumoniae were observed in 534 children and were associated with a Kawasaki-like illness in one child. To date, all of the children are alive. Conclusion: This study underlines the importance of considering, depending on the clinical context, a possible co-infection between SARS-CoV-2 and atypical bacteria, such as Mycoplasma pneumoniae. Co-infections with other respiratory pathogens during the pandemic and hospital stay can cause mistakes in clinical diagnostic and drug treatment. Physicians should perform early differential diagnosis of SARS-CoV-2 in association with other infectious agents. Further studies are needed to have a real incidence of these co-infections and their impact on symptoms, course, and outcome of patients with SARS-CoV-2.

Grasp Stability Prediction for a Dexterous Robotic Hand Combining Depth Vision and Haptic Bayesian Exploration.

Grasp stability prediction of unknown objects is crucial to enable autonomous robotic manipulation in an unstructured environment. Even if prior information about the object is available, real-time local exploration might be necessary to mitigate object modelling inaccuracies. This paper presents an approach to predict safe grasps of unknown objects using depth vision and a dexterous robot hand equipped with tactile feedback. Our approach does not assume any prior knowledge about the objects. First, an object pose estimation is obtained from RGB-D sensing; then, the object is explored haptically to maximise a given grasp metric. We compare two probabilistic methods (i.e. standard and unscented Bayesian Optimisation) against random exploration (i.e. uniform grid search). Our experimental results demonstrate that these probabilistic methods can provide confident predictions after a limited number of exploratory observations, and that unscented Bayesian Optimisation can find safer grasps, taking into account the uncertainty in robot sensing and grasp execution.