Ivan Hernandez Dalas - Mechatronics

Jetson Orin Nano 2 consumes less power at the same performance level of its predecessor. Source: NVIDIA As AI models become more efficient, more devices can become autonomous, but developers need compact, energy-efficient computers built for edge AI, asserted NVIDIA Corp. The company today introduced NVIDIA Jetson Orin Nano 2, a new entry-level computer for edge AI that it said enables millions of developers worldwide to build systems for physical AI applications. “Today’s small and medium frontier models have reached the accuracy of last year’s largest frontier models, unlocking real time intelligence for edge devices,” stated Deepu Talla, vice president of robotics and edge AI at NVIDIA. “The Jetson Orin Nano 2 computer puts that breakthrough within reach of millions of developers, delivering the performance and energy efficiency needed for real-time reasoning in smart drones, robots, and vision AI systems.” “Over the past three months, we’ve seen the evolution of open ...

Kubota unveiled the integrated, autonomous M5 Narrow diesel specialty tractor at CES 2026. | Credit: Kubota Humanoid robots and AI are chasing a powerful promise: replacing repetitive manufacturing tasks for lower cost, easing caregiver shortages, taking people out of dangerous work, and helping more of us live better, fuller lives in environments built for humans. That’s important, and it’s coming. On rural farms or urban construction sites, the problem looks different. The urgent question isn’t “Can a robot live with us?” It’s “can intelligent machines help us keep producing food and building infrastructure when skilled operators are disappearing and margins are razor‑thin?” That’s where embedding physical AI into heavy equipment comes in—and agriculture , especially permanent crops, is where it’s being pushed the hardest. In a vineyard or orchard, a machine operates inches from high‑value plants with almost no room for error. It threads through trellises and under canopies, aro...

XPeng’s Dogotix unit is developing the IRON humanoid robot. Source: XPeng Like other global automakers, XPeng Motors is branching into humanoid robots. The electric vehicle manufacturer today said that its Dogotix robotics unit has raised more than $900 million in its initial funding round, bringing its pre-money valuation to $5 billion and post-transaction valuation to more than $6.3 billion. XPeng said it plans to use the investment to further develop hardware and software, collect data, train physical AI models, build mass-production facilities, and expand globally. Founded in 2016, Shenzhen, China-based Dogotix will remain a subsidiary of XPeng, with He Ziaopeng remaining CEO of both companies. Founded in 2014, Xiaopeng Motors Technology Co. or XPeng said it aspires to become a global leader in AI mobility. The Guangzhou, China-based company develops its full-stack advanced driver-assistance system (ADAS) technology and in-car operating system in-house, and most of its ma...

The MICROBS Lab’s microfliers. 2026 EPFL/MICROBS – CC-BY-SA 4.0. By Celia Luterbacher When you blow air across the neck of a bottle, you’re not just producing a pleasant tone: you’re also demonstrating a phenomenon called Helmholtz resonance. This occurs when airflow passing across an opening causes air trapped inside a cavity (like a glass bottle) to oscillate back and forth. At certain frequencies, these oscillations become much stronger, producing the familiar humming sound. Now, researchers in the MicroBioRobotic Systems ( MICROBS ) Lab in EPFL’s School of Engineering have harnessed the physics behind this phenomenon to build hollow structures that act as miniature, sound-powered machines. The innovation has been published in Science Advances. “Instead of pushing devices around with sound waves, we have created acoustic resonators that are tuned to harness sound at specific frequencies to generate directional thrust and controlled motion,” says lab head Selman Sakar...

RoboDK is an example of digital twin and offline programming software. It allows users to test robots from more than 1,400 brands so they can find the best fits for their applications. Source: RoboDK Manufacturers of all sizes know that automation can help solve labor challenges, increase throughput and quality, reduce waste, and improve consistency. But taking the first step can be daunting, especially for companies that are new to automation. There are thousands of industrial robots available in many shapes and sizes and from many brands, along with thousands of end effectors and peripherals. These elements can be combined in an almost infinite number of ways. Knowing where to start can be a real challenge. What robot size and peripherals are needed? How much floor space will the cell require? Will the throughput justify the investment? How well will it integrate with other machines? Is there a risk of getting locked into a single vendor? How long will a return on investment (ROI)...

A hybrid human-robot workforce requires new metrics, according to HireArt. Source: Lee AI, via Adobe Stock As robotic systems move from pilots into scaled deployments, a pattern is becoming harder to ignore: The limiting factor is rarely the robot itself. It’s the workforce required to operate, maintain, and continuously adapt it in the real world. Most robotics programs begin with a familiar model—small, tightly coordinated teams supporting early deployments. Engineers are close to the system, operators are highly trained, and issues are resolved quickly because everyone is in the loop. That structure works well when there are five or 10 robots in controlled environments. But it starts to break down when deployments scale to dozens of sites across multiple shifts and inconsistent physical environments. At that point, robotics stops behaving like a product launch and starts behaving like a distributed operations business. Physical AI deployments shift labor priorities A useful par...