Robot Misidentifies and Kills Technician in South Korean Agricultural Sorting Facility


A recent study released by Apple reveals that the company is developing a new framework called ELEGNT, aimed at enabling non-anthropomorphic robots to exhibit more natural and expressive movements, which could open new avenues for the future development of home robots. The research team used a robot modeled after the lamp character in Pixar's animation "Luxo Jr.", equipped with a 6-axis robotic arm and a head featuring lighting and projection capabilities. Through the ELEGNT framework, researchers have enabled the robot to not only perform functional tasks but also convey intent and emotion through its movements.
The U.S. is about to witness a revolution in the food industry as CaliExpress becomes the world's first fully robotic and AI-operated restaurant. Located in Pasadena, California, it utilizes Miso Robotics' burger-making robots and Flippy for ordering and cooking. Customers will experience high-tech cooking, with robotic arms and sensor systems accurately preparing delicious burgers and fries. PopID stands out by completing orders through facial recognition, building profiles that include photos, loyalty data, and payment information. Miso Robotics strong.
Boston Dynamics has developed a talking and tour guide robot dog by combining ChatGPT with a physical robot. The robot dog demonstrates self-decision-making abilities, responds to inquiries, and actively interacts with people. This innovation opens up new avenues for the practical application of large language models. Boston Dynamics plans to continue optimizing this product and expanding its applications. This technology has sparked a novel phenomenon in the field of AI for robot dogs.
Tests by the AI coding tool Cursor show that GPT-5.2 excels in complex programming tasks such as building a complete Web browser, with strong logical consistency, task persistence, and engineering understanding, significantly surpassing Claude Opus4.5.
The DeepSeek team launched the Engram module, introducing a 'conditional memory axis' into sparse large language models, aiming to address the issue of computational resource waste when traditional Transformers process repetitive knowledge. This module, as a complement to the mixture-of-experts model, integrates N-gram embedding technology into the model, improving efficiency in processing repetitive patterns.