Machine learning
A new patent granted to John Keith Bentley and his team from Texas Instruments Incorporated has unveiled a novel method for detecting end effector bumps in robots using contact shock transients sensing units. This technology is expected to significantly impact the manufacturing industry, particularly in the semiconductor sector, where precise
Machine learning
A team of researchers from the United Arab Emirates University has made significant strides in forecasting energy commodity prices using Artificial Intelligence-based models. The study, which covered a comprehensive daily dataset from 2006 to 2023, found that machine learning models incorporating global energy transition factors perform better than traditional ANN
Machine learning
Samsung Electronics Co. Ltd. has been granted a patent for an Equipment Front End Module (EFEM) and a destructive analysis automation apparatus, according to a patent published by the United States Patent and Trademark Office (USPTO). The patent, filed on April 1, 2022, and published online on July 1, 2025,
Machine learning
Researchers at Hainan University have proposed an innovative approach to image denoising, leveraging deep learning techniques to improve denoising performance. The study, published in the International Journal of Pattern Recognition and Artificial Intelligence, presents an information-enhanced image denoising framework that incorporates both spatial and frequency-domain features. This method, which integrates
Machine learning
Researchers at the University of Louisville have made a groundbreaking discovery in the field of robotics, developing a local explainability mechanism for robotic grasp failure prediction. This mechanism enhances machine learning transparency at the instance level, addressing the critical need for transparent and reliable grasp failure prediction systems. The study,
Artificial intelligence
Several tech startups have announced their venture capital funding to propel innovation and business growth. Among them, Crash Override, CertiflD, Trusty, Callidus Legal AI, and Xtend Robotics have secured significant funding to scale their platforms and expand their services. Key Takeaways: * Crash Override, a DevOps-focused platform, raised $28 million in
Artificial intelligence
To accelerate the development of robotics and artificial intelligence, a global collaborative ecosystem is crucial, as stated by Wang Xingxing, founder and CEO of Unitree Robotics, at a recent press conference. The company has been at the forefront of open-sourcing its software, allowing clients to develop a wide range of
Machine learning
Research conducted by UiT The Arctic University of Norway has shown that advancements in self-driving cars have significantly enhanced transportation by improving safety, efficiency, and mobility. However, the operation of these vehicles in Arctic environments remains challenging due to harsh weather conditions. To address these challenges, the study integrated LiDAR-based
Machine learning
Researchers at the Institute of Robotics and Intelligent Systems have developed a general framework that integrates visual and acoustic sensor data to improve localization and mapping in complex underwater environments, such as fish farms. The framework enables Unmanned Underwater Vehicles (UUVs) to estimate their net-relative pose and depth within net
Machine learning
A team of researchers from Liaocheng University has proposed a novel algorithm for calibrating robotic geometric errors, achieving higher accuracy in parameter identification. The adaptive momentum Levenberg-Marquardt cascaded B-spline interpolation particle swarm optimization (AMLM-BIPSO) algorithm incorporates a momentum term to suppress overshooting and oscillation, and embeds B-spline interpolation into the
Machine learning
A new research study has unveiled a machine learning architecture that significantly improves breast ultrasound image segmentation accuracy. The novel framework, known as HMA-Net, uses a combination of ConvMixer and ConvNeXT modules, along with convolution-enhanced multihead attention, to effectively capture local and global contextual features. This enables the model to
Machine learning
Researchers at Tsinghua University have published a new study in IEEE Robotics and Automation Letters, highlighting a novel approach to vehicle dynamics modeling in autonomous driving systems. The team proposes a vehicle dynamics correction system that leverages deep neural networks to correct the state residuals of a physical model, thereby
