MAROKO133 Update ai: Humanoid robots get smarter muscles and sharper minds with NVIDIA’s l

📌 MAROKO133 Eksklusif ai: Humanoid robots get smarter muscles and sharper minds wi

Robots just got a new brain and body.

NVIDIA has unveiled a sweeping set of updates that could accelerate the development of humanoid robots and physical AI.

The company announced the open-source Newton Physics Engine, the latest Isaac GR00T N1.6 robot foundation model, and expanded AI infrastructure, all integrated into its Isaac Lab platform.

Together, these technologies give researchers and developers a unified, open, and accelerated robotics stack.

The tools promise to speed up iteration, standardize testing, unify training with on-robot inference, and help robots transfer skills more safely from simulation to the real world.

“Humanoids are the next frontier of physical AI, requiring the ability to reason, adapt and act safely in an unpredictable world,” said Rev Lebaredian, vice president of Omniverse and simulation technology at NVIDIA.

“With these latest updates, developers now have the three computers to bring robots from research into everyday life — with Isaac GR00T serving as robot’s brains, Newton simulating their body and NVIDIA Omniverse as their training ground.”

The updates also underscore NVIDIA’s ambition to define the standard platform for building general-purpose robots, especially humanoids, which demand precise physics and complex reasoning to function in human environments.

Newton redefines robot physics

The beta release of Newton Physics Engine, announced today, is open-source, GPU-accelerated, and managed by the Linux Foundation.

Built on NVIDIA Warp and OpenUSD frameworks and co-developed with Google DeepMind and Disney Research, Newton is designed to handle the extreme demands of humanoid simulation.

With its flexible solvers, Newton allows developers to simulate complex, real-world tasks ranging from walking on snow and gravel to manipulating fragile objects like cups and fruit.

Such capabilities are critical for ensuring that what robots learn in simulation can be reliably deployed in real-world environments.

Early adopters include ETH Zurich’s Robotic Systems Lab, the Technical University of Munich, Peking University, robotics firm Lightwheel, and simulation engine company Style3D.

Brains built for reasoning

Alongside Newton, NVIDIA introduced Isaac GR00T N1.6, an open robot foundation model that integrates NVIDIA Cosmos Reason, a reasoning vision language model designed for physical AI.

Acting as a robot’s “deep-thinking brain,” Cosmos Reason helps humanoids interpret vague instructions, use prior knowledge, and generalize across new tasks.

Cosmos Reason has already been downloaded over 1 million times and tops the Physical Reasoning Leaderboard on Hugging Face.

It is available as a NVIDIA NIM microservice for model deployment. Isaac GR00T N1.6 also expands robots’ ability to move and manipulate objects simultaneously, handling tougher challenges such as opening heavy doors.

Developers can fine-tune GR00T models with the NVIDIA Physical AI Dataset on Hugging Face, which has been downloaded 4.8 million times and now includes thousands of synthetic and real-world trajectories.

Leading robotics firms including AeiROBOT, Franka Robotics, LG Electronics, Lightwheel, Neura Robotics, and Techman Robot are evaluating GR00T models for humanoid development.

NVIDIA also announced Cosmos World Foundation Models for generating synthetic data, a new grasping workflow in Isaac Lab 2.3 tested by Boston Dynamics’ Atlas, and Isaac Lab – Arena, an open framework for scalable evaluation of robot skills.

To power it all, the company introduced AI infrastructure like GB200 NVL72 systems, RTX PRO Servers, and Jetson Thor modules for real-time, on-robot intelligence.

Taken together, these updates position NVIDIA as the hub where physics, reasoning, and AI infrastructure converge, pushing humanoid robotics closer to everyday reality.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Breaking ai: Ultrafast flash heating recovers rare earths from electro

A team of researchers led by Rice University’s James Tour and Shichen Xu has unveiled a lightning-fast method to recover rare earth elements (REEs) from discarded magnets.

The technique promises substantial environmental and economic benefits compared with traditional recycling methods.

Conventional rare earth recycling is energy-intensive and generates toxic waste. The new approach uses flash Joule heating (FJH), which rapidly raises material temperatures to thousands of degrees in milliseconds, combined with chlorine gas to extract REEs in seconds.

The method does not require water or acids, a key improvement for greener processing.

“We’ve demonstrated that we can recover rare earth elements from electronic waste in seconds with minimal environmental footprint,” said Tour, the T.T. and W.F. Chao Professor of Chemistry, professor of materials science and nanoengineering, and study corresponding author.

“It’s the kind of leap forward we need to secure a resilient and circular supply chain.”

The approach aligns with U.S. efforts to strengthen domestic mineral supplies and reduce dependence on imports of critical materials.

Thermodynamics drives selective recovery

The researchers hypothesized that FJH combined with chlorine gas could exploit differences in Gibbs free energy and boiling points to selectively remove non-REE elements from magnet waste.

In practice, iron, cobalt, and other non-REE elements chlorinate and vaporize first, leaving behind the REE oxides. The team tested this on neodymium iron boron and samarium cobalt magnet waste.

By precisely controlling temperatures within seconds, non-REEs converted into volatile chlorides, separating cleanly from the solid REEs.

“The thermodynamic advantage made the process both efficient and clean,” said Xu, the first author of the study and a postdoctoral associate at Rice.

 “This method not only works in tiny fractions of the time compared to traditional routes, but it also avoids any use of water or acid, something that wasn’t thought possible until now.”

Laboratory experiments were complemented by life cycle assessments (LCA) and techno-economic analyses (TEA).

The team achieved over 90 percent purity and yield for REE recovery in a single step. LCA and TEA results showed an 87 percent reduction in energy use, an 84 percent decrease in greenhouse gas emissions, and a 54 percent cut in operating costs compared to hydrometallurgy.

Toward circular rare earth economy

This ultrafast process makes it feasible to build small or large recycling units near electronic waste collection points. Localized systems can process used magnets quickly and cleanly, reducing shipping costs and environmental impact.

“The results show that this is more than an academic exercise — it’s a viable industrial pathway,” Tour said.

Rice University has licensed the intellectual property to Flash Metals USA, a Texas startup that plans to enter production by Q1 2026.

Co-authors include Rice researchers Justin Sharp, Bing Deng, Qiming Liu, Lucas Eddy, Weiqiang Chen, Jaeho Shin, Shihui Chen, Haoxin Ye, Khalil JeBailey, Bowen Li, Tengda Si, and Kai Gong. Funding came from the Defense Advanced Research Projects Agency, the Air Force Office of Scientific Research, and the U.S. Army Corps of Engineers.

This development represents a major step toward a scalable, circular, and environmentally responsible rare earth economy, potentially transforming how critical materials are recovered worldwide.

The study was published in the Proceedings of the National Academy of Sciences.

🔗 Sumber: interestingengineering.com