Designing robots capable of traversing uneven terrain and overcoming physical obstacles has been a longstanding challenge in the field of robotics. Walking robots show promise in this regard due to their agility, redundant DOFs and intermittent ground contact of locomoting appendages. However, the complexity of walking robots and their numerous DOFs make controlling them extremely difficult and computation heavy. Linear policies trained with reinforcement learning have been shown to perform adequately to enable quadrupedal walking, while being computationally light weight. The goal of this research is to study the effect of augmentation of observation space of a linear policy with newer state variables on performance of the policy. Since ground contact and reaction forces are the primary means of robot-environment interaction, they are essential state variables on which the linear policy must be informed. Experimental results show that augmenting the observation space with ground contact and reaction force data trains policies with better survivability, better stability against external disturbances and higher adaptability to untrained conditions.

This research presents preliminary work to address the challenge of identifying at-risk students using supervised machine learning and three unique data categories: engagement, demographics, and performance data collected from Fall 2023 using Canvas and the California State University, Fullerton dashboard. We aim to tackle the persistent challenges of higher education retention and student dropout rates by screening for at-risk students and building a high-risk identification system. By focusing on previously overlooked behavioral factors alongside traditional metrics, this work aims to address educational gaps, enhance student outcomes, and significantly boost student success across disciplines at the University. Pre-processing steps take place to establish a target variable, anonymize student information, manage missing data, and identify the most significant features. Given the mixed data types in the datasets and the binary classification nature of this study, this work considers several machine learning models, including Support Vector Machines (SVM), Naive Bayes, K-nearest neighbors (KNN), Decision Trees, Logistic Regression, and Random Forest. These models predict at-risk students and identify critical periods of the semester when student performance is most vulnerable. We will use validation techniques such as train test split and k-fold cross-validation to ensure the reliability of the models. Our analysis indicates that all algorithms generate an acceptable outcome for at-risk student predictions, while Naive Bayes performs best overall.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. You're gonna need a bigger ... drone. Researchers at California State University, Long Beach-based Shark Lab used drones to study juvenile white sharks along the Southern California coastline and how close they swim to humans in the water. Turns out, it's pretty close.

Whale sharks can carry up to 300 babies at once--at different fetal stages and from different fathers. Zebra sharks experience "virgin birth." These are but a mere sampling of the decade's most fascinating shark discoveries. Some 500 known species of these toothy fish ply our planet's waters, ranging from bite size to bus size, and scientists are still becoming acquainted with most of them. Since 2000, when scientists discovered shark populations were collapsing around the world, research on sharks has ramped up across many fields of study, from paleontology to neuroscience to biomechanics.

But when groups are involved, with many people grabbing the wheel at once, we often find ourselves in a fruitless stalemate headed for disaster, or worse, lurching off the road and into a ditch, seemingly just to spite ourselves. It turns out that Mother Nature has been working on this problem for hundreds of millions of years, evolving countless species that make effective decisions in large groups. A human business team trying to select the ideal location for a new factory would face a similarly complex problem and find it very difficult to choose optimally, and yet simple honeybees achieve this. They do so by forming real-time systems that efficiently combine the diverse perspectives of the hundreds of scout bees that explored the available options, enabling group deliberation that considers their differing levels of conviction until they converge on a single unified decision. It enables groups of all sizes to connect over the internet and deliberate as a unified system, pushing and pulling on decisions while swarming algorithms monitor their actions and reactions.

A team of engineers at California State University, Northridge are developing swarms of tiny, insect-sized robots that could help make exploring other planets safer and more efficient. Led by mechanical engineering professor Nhut Ho, the team was just awarded a $538,000 grant from the US Department of Defense to further develop their miniature robotic space explorers. The longterm goal is to create autonomous swarms of small robots that can move across the surface of other planets to collect samples and complete tasks that might otherwise be too complicated for a rover, or too risky for a human astronaut. 'We were inspired by the behaviors that we see in swarms of ants and bees that self-organize, create clever solutions for different tasks, work in groups of different sizes and have the ability to complete the tasks even when members fail,' Ho told CSU Northridge's news blog. Ho's team will collaborate on the project with another group from the Jet Propulsion Laboratory, which recently won a DARPA competition for autonomous robots completing reconnaissance and search and rescue operations in a simulated disaster area.

Navdeep Gill is a Software Engineer & Data Scientist at H2O.ai where he focuses on model interpretability, GPU accelerated machine learning, and automated machine learning. He graduated from California State University, East Bay with a M.S. degree in Computational Statistics, B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics). During his education, he gained interests in machine learning, time series analysis, statistical computing, data mining, and data visualization. Before joining H2O.ai, he worked at Cisco Systems, focusing on data science and software development. Before stepping into industry he worked in various Neuroscience labs as a researcher/analyst.

Welcome to Essential Education, our daily look at education in California and beyond. California State University's Board of Trustees are meeting Tuesday and Wednesday to discuss graduation rates, executive compensation and the budget shortfall. The L.A. Unified Board of Education's curriculum and special education committees are also meeting today. California State University's Board of Trustees are meeting Tuesday and Wednesday to discuss graduation rates, executive compensation and the budget shortfall. The L.A. Unified Board of Education's curriculum and special education committees are also meeting today.

Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have some features, such as consistent user interaction and hands-free, heads-up operations, which pave the way for gesture recognition methods of text entry. This paper proposes a new text input method for smart watches, which utilizes motion sensor data and machine learning approaches to detect letters written in the air by a user. This method is less computationally intensive and less expensive when compared to computer vision approaches. It is also not affected by lighting factors, which limit computer vision solutions. The AirDraw system prototype developed to test this approach is presented. Additionally, experimental results close to 71% accuracy are presented.