NASA’s Jet Propulsion Laboratory (JPL) has developed a 4-foot-long, four-wheeled prototype designed for high-speed, long-distance autonomous exploration on the Moon and Mars. 

Dubbed Exploration Rover for Navigating Extreme Sloped Terrain (ERNEST), it underwent field tests in the Colorado Desert. The robotic rover successfully traveled 16 miles over 37 hours with minimal human intervention.

“This testing is helping us refine the mobility hardware and autonomy software to navigate extreme distances across a wide range of terrain and lighting conditions anticipated on the Moon,” said Issa Nesnas, a principal technologist at JPL. 

Active suspension system

For the last thirty years, NASA has used a mechanical setup called the “rocker-bogie” suspension system. It is the architecture behind every Mars rover from Sojourner to Perseverance that keeps their wheels grounded.

But it has some problems. Send a passive rover up a loose, sandy slope steeper than 15 degrees, and it will likely get stuck, spin its wheels, or risk a catastrophic tip-over. 

The ERNEST prototype introduces an innovative active suspension system that manages its own weight distribution. 

Equipped with four steerable wheels and a front-powered gimbal, the rover can navigate extreme terrain using unique gaits like squirming, wheel-walking, obstacle-climbing, and even driving sideways. 

Plus, a built-in clutch mechanism allows ERNEST to switch back to a passive mode when efficient, less-demanding driving is needed, blending high-capability mobility with energy conservation.

The 4-foot-long prototype is a testbed designed to advance both robotic autonomy and extreme terrain mobility for future Moon and Mars missions.

ERNEST achieves higher speeds and longer driving distances through its enhanced independent decision-making and a unique ability to lift its individual mesh wheels over obstacles that would trap current Mars rovers.

Ultimately, these technological improvements will inform the design of next-generation rovers capable of exploring previously inaccessible, rough extraterrestrial landscapes.

During recent testing, the vehicle clocked speeds up to 0.6 mph (1 kph) over 37 hours of driving across seven days — an order of magnitude faster than the autonomous navigation speeds of Curiosity and Perseverance. 

“You could do a science road trip across the Moon — or Mars — with this vehicle,” said James Keane, a JPL planetary scientist working on lunar missions.

Preparing for the rough, unknown terrain

Using reinforcement learning, computers ran thousands of hours of virtual simulations over single weekends, punishing the virtual rover with impossible physics scenarios until it learned the optimal way to climb, crawl, and survive.

The software was then loaded directly into the physical rover. Engineers took it out into the desert heat and turned it loose. ERNEST handled the rest. 

The team even ran the rover through the deep, confusing shadows of dusk and dawn. This simulated the extreme lighting conditions future astronauts and robots will face at the Moon’s south pole.

Building on this success, a new project will merge ERNEST’s active suspension control with long-range intelligent navigation. This will allow the rover to plan paths, deciding when to climb surmountable obstacles and when to steer clear of major hazards. 

It will help to prepare future missions for the most rugged landscapes on the Moon and Mars.

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Canadian robotics company Sanctuary AI has reported a significant achievement in industrial automation.

The firm said its system achieved a task success rate of more than 99.5 percent during a complex wire-plugging task for a global Tier 1 automotive supplier. 

The operation completed in 2.54 seconds, meeting the customer’s live-production benchmarks and demonstrating high-speed, reliable performance in a manufacturing setting. 

Sanctuary AI said the milestone highlights its strategy of deploying Physical AI on commercial robots to speed industrial adoption and support future industrial humanoids.

Factory automation leap

The achievement reflects Sanctuary AI’s strategy of deploying its Physical AI platform on existing and next-generation industrial robotic systems, rather than waiting for widespread commercialization of humanoid robots. This hardware-agnostic approach enables the company’s AI models to operate across different robotic platforms while accelerating adoption in manufacturing environments.

Sanctuary AI demonstrated high-performance Physical AI capabilities in an industrial manufacturing environment, achieving a task success rate exceeding 99.5 percent in a complex wire-plugging operation for a global Tier 1 automotive supplier. The system completed the task with a cycle time of 2.54 seconds, matching the throughput requirements of the customer’s live production line and meeting established industrial performance benchmarks.

The deployment focused on a plug-insertion process involving flexible wire assemblies moving along a conveyor. Such tasks are particularly challenging for automation because the cables can bend, twist, and shift position dynamically during transport, requiring continuous perception, adaptation, and precise manipulation.

According to the firm, the AI-powered robotic system successfully detected, tracked, aligned, and inserted the connectors while maintaining production-line speed and reliability.

“Manipulating a flexible wire into a moving target on a live conveyor is exactly the kind of contact-rich dexterity problem that has kept tasks like this out of reach for traditional automation. Solving it required models built around performance from day one with reliability, cycle time, and safety measured against real production benchmarks,” said Olivia Norton, co-founder and CTO of Sanctuary AI, in a statement.

AI power industries

Sanctuary AI develops Physical AI systems designed to enable robots to perform complex tasks in real-world industrial environments. Unlike traditional industrial automation, which relies on fixed programming for repetitive operations, Sanctuary AI’s approach combines advanced perception, reasoning, and manipulation capabilities, allowing robots to adapt to changing conditions and handle tasks that require dexterity and decision-making.

The company’s Physical AI platform processes information from cameras, sensors, and robotic actuators to understand its surroundings and execute actions in real time. This enables robots to identify objects, track movement, plan motions, and manipulate components with a high degree of precision. The technology is particularly suited to manufacturing environments where variations in object position, orientation, or movement make conventional automation difficult.

A key feature of Sanctuary AI’s strategy is its hardware-agnostic design. The AI can be deployed on existing industrial robotic systems as well as future robotic platforms, reducing the need for specialized hardware. This allows manufacturers to integrate advanced AI capabilities into current production lines while preserving existing infrastructure investments.

The platform is designed to automate labor-intensive tasks, including assembly, material handling, inspection, and component insertion. By combining real-time perception, motion planning, and adaptive control, Sanctuary AI aims to help factories improve productivity, maintain consistent quality, address labor shortages, and prepare for the next generation of intelligent industrial robots.

“By focusing on a performance-first approach to Physical AI models, we’re delivering value to customers today, on an AI platform that will also scale to the next generation of general-purpose systems,” said Norton, in a statement.

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Sen. Bernie Sanders has unveiled a proposal that would give Americans a direct financial stake in the country’s booming artificial intelligence industry. The Vermont independent introduced legislation Thursday that would create a national sovereign wealth fund tied to major AI companies. Under the proposal, the federal government would hold a 50% ownership stake in qualifying AI firms and distribute annual payments to U.S. citizens.

Sanders argues that today’s AI systems rely heavily on content, knowledge, and creative works produced by generations of people. Because AI companies benefit from that collective human output, he says the public should share in the industry’s financial gains. The legislation, called the American AI Sovereign Wealth Fund Act, comes as lawmakers debate how to regulate AI and address concerns about wealth concentration in the technology sector.

Public stake in AI

The proposal would apply to AI companies generating more than $200 million in annual revenue. Those firms would become subject to a system that grants the public a significant ownership interest through a federally managed investment fund. Sanders estimates the fund could reach a value of roughly $7 trillion based on current valuations of leading U.S. AI developers.

The future of AI must not be decided behind closed doors by billionaires seeking to maximize their power and profit.

It should be decided by the American people.

That's why I'm introducing the American AI Sovereign Wealth Fund Act. pic.twitter.com/sbC0YMT90f

— Sen. Bernie Sanders (@SenSanders) June 18, 2026

The senator says the structure would allow Americans to benefit directly from one of the fastest-growing industries in the economy. He projects that citizens could initially receive annual payments of around $1,000. Those payouts could increase as the AI sector expands.

Speaking to reporters, Sanders said the payments would likely grow if AI companies continue generating larger profits. He also suggested future revenue could help support public priorities such as healthcare, education, and housing.

Collective value argument

At the center of the proposal is Sanders’ belief that AI companies derive value from information created by society as a whole. The legislation argues that AI models train on vast amounts of human-created material, including books, journalism, software code, scientific research, artwork, videos, and other forms of knowledge.

Sanders contends that a small group of technology executives now captures most of the economic benefits generated from those resources. Supporters of the measure compare AI to publicly owned natural resources. In that view, companies extracting value from a shared asset should return a portion of that wealth to the public.

Model draws from funds

The bill takes inspiration from sovereign wealth funds already operating across the United States and around the world. States such as Texas and New Mexico have long used investment funds supported by natural resource revenues to finance public programs, particularly education. Dozens of countries, including Norway, China, and the United Arab Emirates, also operate sovereign wealth funds that manage public assets and investments.

If Congress approves the legislation, a seven-member independent commission would oversee the fund. The president would nominate commissioners, while the Senate would confirm them.

The commission would represent public interests in qualifying AI companies and manage the government’s ownership stake. While the proposal faces significant political hurdles, it adds a new dimension to the growing debate over who should benefit from the economic gains created by artificial intelligence.