Researchers have succeeded in developing the smallest fully autonomous robot in history. It measures less than 1 millimeter and can swim underwater for months powered only by light. Miniaturization has long been a challenge in the history of robotics . While engineers have made great strides in the miniaturization of electronics in the past few decades, builders of miniature autonomous robots have not been able to meet the goal of getting them under 1 millimeter in size. This is because small arms and legs are fragile and difficult to manufacture.

Gaylord is home to its own herd of 60 elk and one of the largest wild herds in the United States. Breakthroughs, discoveries, and DIY tips sent every weekday. When winter's bitter winds blow and snow falls, it can be hard for some of us to muster up the will and energy to actually spend time out in nature. Still, connecting with nature is important for our health, even in cold weather. Now, viewers around the world can take advantage of Gaylord, Michigan's elk cam and get a taste of the outdoors from the comfort of home.

Tue 16 Dec 2025 08.00 ESTLast modified on Tue 16 Dec 2025 08.02 EST The sign outside Tom Hermes's farmyard in Perkins Township in Ohio, a short drive south of the shores of Lake Erie, proudly claims that his family have farmed the land here since 1900. Today, he raises 130 head of cattle and grows corn, wheat, grass and soybeans on 1,200 acres of land. For his family, his animals and wider business, water is life. So when, in May 2024, the Texas-based Aligned Data Centers broke ground on its NEO-01, four-building, 200,000 sq ft data center on a brownfield site that abuts farmland that Hermes rents, he was concerned. "We have city water here. That's going to reduce the pressure if they are sucking all the water," he says of the data center.

How the Witch of November doomed the'Edmund Fitzgerald' Fifty years after the Great Lakes freighter sank, scientists can explain the weather that still haunts Lake Superior. When the SS Edmund Fitzgerald left port on November 10, 1975, there was no way for the crew to know what they were sailing into. Breakthroughs, discoveries, and DIY tips sent every weekday. On the afternoon of November 9, 1975, when the set out on its 746-mile run from Superior, Wisconsin, to Detroit, Michigan, Lake Superior was mostly calm. Even so, the crew likely saw the red sky from the intensifying storm gathering over the Great Plains.

Sea lampreys--invasive, leechlike creatures that once nearly destroyed the Great Lakes' fishing economy--are kept in check by a small U.S.-Canadian program. Will it survive Trump's slash-and-burn campaign? Ally Porter walked ahead of me as we sidestepped down a steep, loamy embankment. Our path lit only by headlamps, a waning sliver of moon, and what seemed to be thousands of stars, we made our way to a mucky riverbank about twenty feet below. At one point, I lost my footing and ended up wedged against a tree trunk. Porter, who had two tight braids that landed just below her shoulders, kept going. She moved with ease through several inches of sludge, toward a yellow glow stick tied to a tree at the water's edge.

Claire chatted to Chad Jenkins from University of Michigan about how robots can learn from people and assist us in our daily lives. Odest Chadwicke Jenkins is a Professor of Robotics and a Professor of Electrical Engineering and Computer Science at the University of Michigan. His research addresses problems in robot learning from demonstration and human-robot interaction, primarily focused on dexterous mobile manipulation and robot perception. In 2022, he founded the Robotics Major Degree Program for undergraduates at the University of Michigan. He was awarded the 2024 ACM/CMD-IT Richard A. Tapia Achievement Award for Scientific Scholarship, Civic Science, and Diversifying Computing.

Vehicle crashes involve complex interactions between road users, split-second decisions, and challenging environmental conditions. Among these, two-vehicle crashes are the most prevalent, accounting for approximately 70% of roadway crashes and posing a significant challenge to traffic safety. Identifying Driver Hazardous Action (DHA) is essential for understanding crash causation, yet the reliability of DHA data in large-scale databases is limited by inconsistent and labor-intensive manual coding practices. Here, we present an innovative framework that leverages a fine-tuned large language model to automatically infer DHAs from textual crash narratives, thereby improving the validity and interpretability of DHA classifications. Using five years of two-vehicle crash data from MTCF, we fine-tuned the Llama 3.2 1B model on detailed crash narratives and benchmarked its performance against conventional machine learning classifiers, including Random Forest, XGBoost, CatBoost, and a neural network. The fine-tuned LLM achieved an overall accuracy of 80%, surpassing all baseline models and demonstrating pronounced improvements in scenarios with imbalanced data. To increase interpretability, we developed a probabilistic reasoning approach, analyzing model output shifts across original test sets and three targeted counterfactual scenarios: variations in driver distraction and age. Our analysis revealed that introducing distraction for one driver substantially increased the likelihood of "General Unsafe Driving"; distraction for both drivers maximized the probability of "Both Drivers Took Hazardous Actions"; and assigning a teen driver markedly elevated the probability of "Speed and Stopping Violations." Our framework and analytical methods provide a robust and interpretable solution for large-scale automated DHA detection, offering new opportunities for traffic safety analysis and intervention.

We then prompt-engineered GPT-4 (ChatGPT 4.0) to generate descriptive text labels for each image, creating 300 image-text labels for training. The CoFRIDA framework provided stroke-level painting data by simulating the robot's painting process, generating both partial-progress and completed canvases [1], [11]. While CoFRIDA is originally designed to produce paintings that accurately depict their subject matter with coherent stroke patterns and layering, our work adapts this approach specifically for abstract art by fine-tuning the InstructPix2Pix Module on a curated dataset of abstract works from Kaggle paired with GPT -4-generated captions. This specialization enables the model to generate abstract-style transformations suited for robotic execution in iterative and collaborative painting scenarios. I. Artistic Framework The robot was programmed to draw circle-inspired shapes in the first session, square-inspired in the second, and triangle-inspired in the third. This setting was inspired by Bruno Munari's [17] exploration of geometric forms (see Figure 6). In the 1960s, Italian designer Bruno Munari published visual case studies on Circle, Square, and later Triangle, associating each with specific qualities: the circle with the Divine, the square with safety, and the triangle as a key connective form.

Abstract--This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the human driver collaborates with an autonomous driving system to control the steering of a vehicle simultaneously. Advanced driving skills are those necessary to safely push the vehicle to its handling limits in high-performance driving such as racing and emergency obstacle avoidance. Previous research has demonstrated the performance and safety benefits of shared control schemes using both subjective and objective evaluations. However, these schemes have not been assessed for their impact on skill acquisition on complex and demanding tasks. Prior research on long-term skill acquisition either applies haptic shared control to simple tasks or employs other feedback methods like visual and auditory aids. T o bridge this gap, this study creates a cyber racing coach framework based on the haptic shared control paradigm and evaluates its performance in helping human drivers acquire high-performance driving skills. The framework introduces (1) an autonomous driving system that is capable of cooperating with humans in a highly performant driving scenario; and (2) a haptic shared control mechanism along with a fading scheme to gradually reduce the steering assistance from autonomy based on the human driver's performance during training. Two benchmarks are considered: self-learning (no assistance) and full assistance during training. Results from a human subject study indicate that the proposed framework helps human drivers develop superior racing skills compared to the benchmarks, resulting in better performance and consistency. Advanced driving skills refer to a set of competencies that go beyond basic driving abilities in terms of situational awareness, hazard perception, risk management, and vehicle handling [1]. They are crucial in high-performance driving tasks such as racing, and can also improve safety in everyday driving [1], [2]. This work has been submitted to the IEEE for possible publication.

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