Ivan Hernandez Dalas - Mechatronics

Models can generate actions, but hardware must execute those actions. | Credit: OnRoot Physical AI promises robots that can perceive, act and adapt in the real world with far less task-specific engineering than traditional automation. It’s a compelling proposition: robots powered by AI models trained on vast amounts of data, capable of improving over time and operating effectively in far less structured environments. But how do we get there? As AI systems move from the digital to the physical world, new requirements emerge. Intelligent models and policies remain essential, but they are only a part of the equation. Robots ultimately interact with the physical world through grippers, sensors and tools that make direct contact with objects. For physical AI to deliver on its promise, it needs a reliable physical interaction layer: End-of-arm tooling (EOAT) that combines adaptability, sensing and feedback so robots can respond effectively to uncertainty and variation. In selecting the ...

Reframe Systems combines robotics, Pixels to Parts software, and a network of microfactories to build homes. | Source: Reframe Systems Reframe Systems, a robotics company using physical AI to industrialize home construction, today announced said it has raised $40 million. The company said the funding will help it scale its microfactory network across North America. Currently, the U.S. is short 4.5 million homes. Reframe said this problem stems from the construction industry’s fragmented nature. Most houses are build by hand, involving 25 or more subcontractors that are drawing on a shrinking pool of skilled trades. Reframe hopes to address this issue by building a network of automated microfactories near communities where homes are needed. This allows the company to adapt production to different zoning requirements, climate conditions, and architectural styles. “The housing shortage is an abstraction until you’re the person who can’t find a home in the communit...

Photo by Alex Knight on Unsplash . By Hasan Ayaz , Drexel University ; Ewart J. de Visser , United States Air Force Academy ; Frank Krueger , George Mason University , and Yigit Topoglu , United States Air Force Academy People become more suspicious of a humanoid robot that makes errors, especially when the robot is an expressive conversation partner. In our new study published in the journal Science Robotics, we had 50 people hold conversations and make joint decisions with the commercial humanoid robot Pepper , which is designed to be expressive and recognize emotions. Sometimes we had the robot give sound advice. Sometimes we had it make conversational mistakes, interrupting people or pushing illogical suggestions. For some participants, the robot was animated, using gestures, eye contact and nods. For others, it stayed motionless. We measured four things: brain activity, levels of the hormone oxytocin, self-reported trust and our observations of the robot’s influence on part...

The ‘edge AI wall.’ Click here to enlarge. Credit: Zhengis Tileubay My previous article for The Robot Report examined the problem of computational instability in autonomous mobile robots, or AMRs. Since then, the rapid development of artificial intelligence over the past year has led to a broader perspective about edge AI. The article, “ Phase stability regulator based on two dynamic parameters for autonomous mobile robots ,” focused on situations in which a machine operates in a complex, rapidly changing environment characterized by a progressive growth of alternative trajectories and scenarios. In such cases, sensors , actuators , and core software may remain fully operational, yet the quality of decision-making starts to deteriorate. My main argument was that behavioral degradation often results not from hardware failure, but from an information overload within the planner, which is forced to evaluate an excessive number of alternatives in real time, leading to redu...

A small sample of Amazon’s vast deployed warehouse fulfillment fleet. Source: Amazon Swarm robotics systems rely on power architecture because energy distribution influences coordination efficiency and fault tolerance in multi-agent environments. Centralized models depend on unified infrastructure and coordinated energy management. Meanwhile, decentralized approaches distribute power control and operational decision-making across individual robotic units within the swarm. These architectural differences create significant operational trade-offs involving communication latency, synchronization precision, and adaptive responsiveness. They make power topology an important consideration for robotics engineers, artificial intelligence researchers, and industrial automation professionals. Power architecture is a core layer in swarm intelligence Power topology is central to swarm coordination because autonomous decision-making and scalable task execution depend on how robotic agents d...

Danny Bernstein (l) from Reservoir and Sean Sundberg (r) from John Deere announced a partnership at the Ruggedize event. | Credit: Reservoir Deere & Co. last week posted Q3 2026 net income of $1.379 billion, or $5.10 per share, outperforming market expectations despite headwinds in its Production and Precision Agriculture division—the primary unit driving revenue from the company’s autonomous machinery, smart targeting systems, and precision technologies.   Production and Precision Agriculture Q3 breakdown: Small Agriculture and Turf sales increased for the quarter. Net sales were up 12%, and profit was up 28%.   Production and Precision Agriculture sales decreased for the quarter as a result of lower shipment volumes, partially offset by favorable price realization and foreign currency translation. Net sales were down 6%, and profit was down 9%. The revised outlook signaled that the multi-year downturn in global farm machinery demand may finally be reaching its floor...