Ivan Hernandez Dalas - Mechatronics

MIT researchers utilized specially trained generative AI models to create a system that can complete the shape of hidden 3D objects, like the ones pictured. Credit: Courtesy of the researchers . By Adam Zewe MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot’s ability to reliably grasp and manipulate objects that are blocked from view. This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model....

Industrial robot deployments have continued to climb across much of the world. Click here to enlarge. Source: International Federation of Robotics. Factories worldwide are implementing more automation, according to the “World Robotics 2025” report from the International Federation of Robotics, or IFR. The organization said the robot density, or number of units per 10,000 employees, has risen across three continents. The IFR noted that Western Europe in particular increased its robot density to 267 robots per 10,000 employees in 2024 — ahead of North America with 204 units and Asia with 131 units. “The robot density metric provides a uniform basis for comparison by relating the total number of robots used in a country to its economic size, as measured by its workforce,” stated Takayuki Ito, president of the IFR. The Frankfurt, Germany-based organization previously reported that average global robot density had doubled from 2014 to 2024. The IFR explained that its measurement ca...

AGIBOT says Genie Sim 3.0 signals a shift toward treating simulation not as a tool, but as a foundation for developing and evaluating embodied AI at scale. Source: AGIBOT While recent progress in robotics has been driven by advances in models and algorithms, real-world deployment continues to be constrained by high data collection costs, limited scenario diversity, and fragmented benchmarking standards, according to AGIBOT. The company today said it has upgraded its Genie Sim 3.0 development environment. AGIBOT said its platform now addresses three long-standing bottlenecks in embodied AI : environment generation, data scalability, and standardized evaluation. The Shanghai-based company  said it designed Genie Sim 3.0 to integrate scene generation, simulation, data, and evaluation into a unified, reusable infrastructure. Genie Sim World generates environments from language Genie Sim 3.0 introduces a spatial world model that allows users to generate fully interactive 3D envi...

Robots that OLogic has consulted on, from left to right, Bear Robotics, JIBO, Friendly Robot Co., and Cobalt Robotics. | Source: OLogic In a world where software dominates the headlines, robotics remains one of the few industries where hardware still truly matters. At the Robotics Summit & Expo , which takes place on May 27 and 28 in Boston, attendees can learn from OLogic how to balance hardware and software for real-world success. Drawing on over two decades of experience helping companies from startups to Fortune 500s bring their ideas to life, Ted Larson, the co-founder and CEO of OLogic, will share what makes a great robotics company and what separates successful robots from those that never make it past the prototype stage. This session, “Balancing Hardware and Software for Real-World Success,” will explore how the best robotics companies think about design, collaboration, and manufacturability from Day 1. Attendees will learn why hardware and software must evolve togeth...

AGIBOT’s robot has a dexterous design to collect and use data. Source: AGIBOT As robotics research moves beyond controlled lab settings into real-world environments, the demand for large-scale, high-quality data has become increasingly critical, according to AGIBOT. The company today released AGIBOT WORLD 2026, an open-source heterogeneous dataset it said is designed to systematically support five key research pathways in embodied intelligence. “The dataset features structured, high-quality, and precisely annotated real-world robot data, providing developers and researchers with a robust foundation for training next-generation embodied AI systems,” said AGIBOT. Editor’s note:  At the 2026  Robotics Summit & Expo on May 27 and 28 in Boston, there will be sessions on embodied and physical AI , as well as on humanoid robot development.  Registration is now open. AGIBOT WORLD follows free-form data-collection strategy AGIBOT WORLD 2026 spans a wide range of rea...

In the latest in our series of interviews meeting the AAAI/SIGAI Doctoral Consortium participants, we caught up with Aniket Roy to find out more about his research on generative models for computer vision tasks. Tell us a bit about your PhD – where did you study, and what was the topic of your research? I recently completed my PhD in Computer Science at Johns Hopkins University, where I worked under the supervision of Bloomberg Distinguished Professor Rama Chellappa. My research primarily focused on developing methods for resource-constrained image generation and visual understanding. In particular, I explored how modern generative models can be adapted to operate efficiently while maintaining strong performance. During my PhD, I worked broadly at the intersection of generative AI, multimodal learning, and few-shot learning. Much of my work involved designing techniques that enable models to learn new concepts or perform complex visual tasks with limited data or computational res...