Ivan Hernandez Dalas - Mechatronics

Physical AI needs more than data to enable robots to be more effective. Source: Erika AI, via Adobe Stock The world of artificial intelligence is moving from chatbots to vision processing—AI that lives in robots and self-driving cars. While we have made major strides in training these systems using massive datasets and digital simulations, a critical gap remains: the bridge between what a robot “sees” and what is actually happening in our messy, physical world. High-level reasoning is not enough if the system doesn’t fully understand the physical state of its environment. Physical AI evolves from Version 1.0 to 2.0 Currently, the industry is dominated by “physical AI 1.0.” This phase is defined by scale: using massive amounts of video and text data, along with hyper-realistic simulations like NVIDIA ’s Cosmos platform, to teach machines how the world works before they ever take their first steps. However, physical AI 1.0 has a “vision-first...

Image credit: By Jeff Dahl – Own work by uploader, Based on the public domain document: [1] , CC BY-SA 3.0 , Link By Stephanie Parker This research from the NeuroAI Lab of Martin Schrimpf, part of EPFL’s Schools of Computer and Communication Sciences and Life Sciences , uses AI models to predict exactly where to stimulate the brain to evoke images of faces and specific objects in the users instead of simply evoking spots of light. The models developed at EPFL were used by Dutch researchers for live trials on sighted monkeys. The preliminary results, presented in April at the International Conference on Learning Representations , show very promising implications for vision in humans as well. “The motivation for this project is that there are many people with visual deficits that are irreparable, in the sense that somewhere along the visual processing stream, starting with the retina, there is a deficit which cannot be repaired,” says Johannes Mehrer, a scientist in the NeuroA...

Eclipse RealitySync helps manufacturers evaluate, de-risk, and scale automation faster. | Source: Eclipse Automation Eclipse Automation launched Eclipse RealitySync at Automate 2026. The company said this product offers a new approach to how manufacturers evaluate, de-risk, and scale factory automation. Factory automation has grown more complex, connected, and harder to assess using traditional methods. Eclipse RealitySync allows manufacturers to step into their future factory before it’s built. The system uses immersive environments powered by Apple Vision Pro technology and guided by Eclipse Automation experts. With RealitySync, manufacturers can walk production lines, explore workflows in context, and collaborate across teams in a highly connected and interactive way. “The way manufacturers evaluate automation hasn’t kept pace with the real-world complexity of modern production,” said Steve Mai, the CEO of Eclipse Automation . “Teams are still making major investment decisions ...

Agility Robotics is the first to use NVIDIA Halos for Robotics to build safety into its humanoids working in factories, warehouses, and logistics operations for customers including Amazon, GXO, Schaeffler, and Toyota Motor Manufacturing Canada. | Source: NVIDIA NVIDIA today launched NVIDIA Halos for Robotics. This is a full-stack, comprehensive safety system for robotics and physical AI that unifies AI compute and safety. NVIDIA said autonomous robots will need AI foundation models, accelerated compute, and distributed sensors to operate in dynamic environments alongside humans. Scaling these systems requires a full-stack safety architecture. NVIDIA said Halos enables companies to rely on a standardized, unified safety architecture that connects AI compute, system software, sensor data, safety applications, and inspection for robotic systems. “Physical AI is transforming how factories, warehouses, and logistics operations work, and robotics teams need a unified safety architectur...

Collaborative Robtics’ Proxie 2.0 offers bimanual manipulation and autotasking. | Credit: Collaborative Robotics Collaborative Robotics (Cobot) unveiled the second-generation version of its Proxie mobile robot, adding greater payload capacity, self-swapping batteries, autonomous task identification, and a new two-armed manipulation option as the company looks to expand deployments across healthcare, logistics, and manufacturing. The Santa Clara, Calif.-based company introduced Proxie Gen 2 at Automate 2026, positioning the system as a production-proven mobile manipulator. “For decades, deploying robots has meant choosing between mobility and dexterity, and always required custom software integration,” said Collaborative Robotics founder and CEO Brad Porter. “Our second-generation Proxie brings all of that together in one platform we designed end-to-end.” Lessons from field shape Proxie Gen 2 Cobot has spent the past two years quietly gathering operati...

Gudel will demonstrate a grinding application at Automate 2026. Source: Titan Robotics Grinding and surface finishing of fabricated parts is one of the classic dull, dirty, or dangerous applications that are suitable for robot automation. Unfortunately, this requires multiple robots and complex part repositioning, which adds significant cost and extends cycle times while introducing new sources of variation in the process, noted Güdel AG. At Automate 2026, the company plans to show a system designed to scale beyond these physical constraints. “Expanding the robot’s workspace isn’t just a helpful addition; it’s the factor that finally makes automation feasible for large, difficult-to-reach parts,” said Brenda Courim, director of sales and marketing at Güdel US. Güdel Inc. is the U.S. subsidiary of Güdel Group, a Langenthal, Switzerland-based manufacturer of automation products, systems, and services. The company supplies linear motion modules, robot track motion units, gantry ...