Breakthroughs, discoveries, and DIY tips sent every weekday. A Jetsons-style Rosey household assistant robot may finally be a reality--only it probably doesn't look quite like you would expect. In fact, the contraption in question more closely resembles a custodian's mop and bucket. Though it might not be much of a looker, researchers from the Suzhou Industrial Park Institute of Vocational Technology and Xi'an Jiaotong-Liverpool University in China say their robot design can autonomously steer clear of most large furniture and children. It can even pick up loose toys and sort through smelly socks--at least some of the time.

It's not quite jetpacks and robot butlers, but the 2025 version of a futuristic office involves ditching your clunky scanner for good. Whether you're scanning school documents, calculating receipts totals, or just tired of your bulky scanner hogging desk space, iScanner makes the process fast, easy. Unlike apps like Adobe Scan or CamScanner, it is subscription-free. It's powered by AI to automatically detect borders, straighten scans, and clean up images for crisp, professional-looking files. Additionally, iScanner comes with a full mobile PDF editor--so you won't have to upload your docs to a sketchy third-party website to sign a form.

--Small-scale autonomous vehicle platforms provide a cost-effective environment for developing and testing advanced driving systems. However, specific configurations within this scale are underrepresented, limiting full awareness of their potential. This paper focuses on a one-sixth-scale setup, offering a high-level overview of its design, hardware and software integration, and typical challenges encountered during development. We discuss methods for addressing mechanical and electronic issues common to this scale and propose guidelines for improving reliability and performance. By sharing these insights, we aim to expand the utility of small-scale vehicles for testing autonomous driving algorithms and to encourage further research in this domain.

Robots are inching closer to being helpers in our homes. Remember Rosie from "The Jetsons?" For those too young, Rosie was a futuristic robot helper in a classic cartoon. Now, the idea of having such a robot in our homes feels like it's inching closer to reality with the unveiling of NEO Gamma. Developed by the artificial intelligence company 1X, this isn't your clunky, metallic automaton.

The ubiquitous deployment of deep learning systems on resource-constrained Edge devices is hindered by their high computational complexity coupled with their fragility to out-of-distribution (OOD) data, especially to naturally occurring common corruptions. Current solutions rely on the Cloud to train and compress models before deploying to the Edge. This incurs high energy and latency costs in transmitting locally acquired field data to the Cloud while also raising privacy concerns. We propose GEARnn (Growing Efficient, Accurate, and Robust neural networks) to grow and train robust networks in-situ, i.e., completely on the Edge device. Starting with a low-complexity initial backbone network, GEARnn employs One-Shot Growth (OSG) to grow a network satisfying the memory constraints of the Edge device using clean data, and robustifies the network using Efficient Robust Augmentation (ERA) to obtain the final network. We demonstrate results on a NVIDIA Jetson Xavier NX, and analyze the trade-offs between accuracy, robustness, model size, energy consumption, and training time. Our results demonstrate the construction of efficient, accurate, and robust networks entirely on an Edge device.

Deep State-Space Models (SSM) demonstrate state-of-the art performance on long-range sequence modeling tasks. While the recurrent structure of SSMs can be efficiently implemented as a convolution or as a parallel scan during training, recurrent token-by-token processing cannot currently be implemented efficiently on GPUs. Here, we demonstrate efficient token-by-token inference of the SSM S4D on Intel's Loihi 2 state-of-the-art neuromorphic processor. We compare this first ever neuromorphic-hardware implementation of an SSM on sMNIST, psMNIST, and sCIFAR to a recurrent and a convolutional implementation of S4D on Jetson Orin Nano (Jetson). While we find Jetson to perform better in an offline sample-by-sample based batched processing mode, Loihi 2 outperforms during token-by-token based processing, where it consumes 1000 times less energy with a 75 times lower latency and a 75 times higher throughput compared to the recurrent implementation of S4D on Jetson. This opens up new avenues towards efficient real-time streaming applications of SSMs.

With the rapid advances in deep learning and smart manufacturing in Industry 4.0, there is an imperative for high-throughput, high-performance, and fully integrated visual inspection systems. Most anomaly detection approaches using defect detection datasets, such as MVTec AD, employ one-class models that require fitting separate models for each class. On the contrary, unified models eliminate the need for fitting separate models for each class and significantly reduce cost and memory requirements. Thus, in this work, we experiment with considering a unified multi-class setup. Our experimental study shows that multi-class models perform at par with one-class models for the standard MVTec AD dataset. Hence, this indicates that there may not be a need to learn separate object/class-wise models when the object classes are significantly different from each other, as is the case of the dataset considered. Furthermore, we have deployed three different unified lightweight architectures on the CPU and an edge device (NVIDIA Jetson Xavier NX). We analyze the quantized multi-class anomaly detection models in terms of latency and memory requirements for deployment on the edge device while comparing quantization-aware training (QAT) and post-training quantization (PTQ) for performance at different precision widths. In addition, we explored two different methods of calibration required in post-training scenarios and show that one of them performs notably better, highlighting its importance for unsupervised tasks. Due to quantization, the performance drop in PTQ is further compensated by QAT, which yields at par performance with the original 32-bit Floating point in two of the models considered.

"She cooks, she cleans, and she still finds time to play ball with Elroy," George and Jane Jetsons' six-and-a-half-year-old son. Set in the year 2062 and described in the 1960s animated sitcom The Jetsons as an "aluminum-encased, battery-powered robotic maid" who is the "perfect answer for any modern family," Rosie the Robot takes care of chores around the house while also serving as friend and confidante of mother Jane. Sarcastic and funny, Rosie is a hardworking nanny and aunt figure to children Elroy and Judy. While many technologies The Jetsons predicted for 2062 have become reality, such as video calls and smart watches, the full realization of robots as the 1960s ideal friend and helper who makes life easier has yet to be fulfilled. For twenty-five years, roboticist Daniel Theobald has been on a mission to create robots that can solve the world's most pressing problems.

Choosing a platform to work with Computer Vision on the Edge is difficult. There are dozens of boards on the market. If you read about one of them, you want to use it. But when you try - it is not so good. I tried to compare a lot of the cheap boards on the market.

On the evening of Sunday, Sept. 23, 1962, millions of American families finished their dinners, turned on their televisions and were introduced to The Jetsons, a cartoon sitcom produced by the legendary team of Hanna-Barbera. Set in 2062, The Jetsons captured the technological optimism of the time and projected it into a space-age, gadget-fueled vision of the future, inviting its viewers to imagine the dazzling possibilities that the current wave of technological achievement could one day realize. In the end, The Jetsons was a rather tame, pedestrian sitcom about a family that reinforced traditional gender and family roles, knew little of the social issues of the time (it was, for example, unbearably white), and effectively glorified the consumerist, suburban lifestyle. But as a template for a technology-driven American future, it was no less than iconic. The Jetsons debuted five years after the Soviets had launched Sputnik, four years after the opening of the first commercial nuclear power plant in the U.S., and 16 months after President John F. Kennedy set a goal of putting a man on the moon by the decade's end. Fifteen years earlier, scientists at AT&T's Bell Labs invented the transistor, and soon after, miniature (by contemporary standards) transistor radios were found in many households.