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.

A Seattle-based robotics company has developed a three-foot-tall humanoid robot designed for use in public attractions and venues.

The AI-powered robot named Codey can navigate autonomously, engage in conversations without remote human control, and make independent decisions.

While not yet capable of performing complex physical tasks such as fetching objects, Codey is being developed for future roles in healthcare, education, and hospitality.

The company says the robot is designed to interact naturally with people, particularly children, in public-facing environments.

Codey targets companionship

Codey is designed to serve as an interactive companion in public-facing environments. Standing about three feet tall, the robot features glowing hazel eyes, expressive facial animations, and a friendly appearance intended to engage users, particularly children.

Powered by AI, Codey can navigate autonomously, hold conversations, and make basic decisions without requiring a remote human operator. Unlike many robots that rely heavily on teleoperation, Codey is designed to function independently, enabling more natural and responsive interactions with people. The robot uses a combination of sensors, onboard computing, and AI software to understand its surroundings and communicate with users in real time.

Mind Children plans to conduct pilot studies this year in museums, hotels, and art galleries to gather data and refine Codey ahead of mass production. One of the company’s near-term objectives is to enable the robot to perform simple physical tasks, such as pressing buttons. A second-generation version of Codey is scheduled for release in 2027. To support development, Mind Children has launched a crowdfunding campaign and has raised more than $600,000 toward its $1 million funding target.

In the longer term, Codey is expected to be introduced into healthcare, education, and hospitality settings. Potential applications include assisting visitors with information, providing companionship, supporting learning activities, and helping organizations deliver more engaging customer experiences.

Embodied AGI advances

At the core of Mind Children’s robotics platform is Hyperon, the decentralized artificial general intelligence (AGI) framework developed by SingularityNET. The system is designed to give robots advanced cognitive capabilities that go beyond traditional rule-based automation, enabling them to reason, learn, adapt, and make decisions in dynamic real-world environments.

By integrating Hyperon into its humanoid robots, Mind Children aims to create machines capable of understanding context, processing complex information, and responding more naturally to human interactions. The framework incorporates knowledge representation, reasoning engines, and human-inspired motivation systems that help robots prioritize tasks, pursue goals, and adapt their behavior based on changing circumstances. This approach is intended to move robots closer to exhibiting flexible, human-like intelligence rather than performing only pre-programmed actions.

Unlike conventional AI systems that rely on centralized computing and data control, Hyperon operates within SingularityNET’s decentralized ecosystem. This architecture allows AI services, knowledge resources, and computational capabilities to be distributed across a network, promoting collaboration, scalability, and interoperability among different AI agents and robotic platforms.

The partnership between Mind Children and SingularityNET positions the company among the first developers pursuing embodied decentralized AGI—an emerging field that combines advanced artificial intelligence with physical robotic systems. The goal is to create autonomous robots capable of perceiving, reasoning, learning, and interacting with the physical world while continuously improving their capabilities through decentralized intelligence networks.

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US robotics firm Faraday Future (FF) has unveiled its first-half embodied AI (EAI) robotics lineup, introducing a full-form Robot World that spans six major product series.

The company also launched what it describes as the world’s first Three-in-One EAI Robotics Education Ecosystem Strategy, aimed at connecting robotics education, industry, and consumer applications.

During an event at its new headquarters in El Segundo, California, Faraday Future debuted the Futurist humanoid robot and the FX Navi quadruped robot, with the latter priced at $1,990 and available for immediate sale and delivery.

The California-based firm also announced the FF Robotics Partner Program, designed to expand global collaboration across its robotics ecosystem.

Robotics ecosystem unveiled

FF has unveiled a broad EAI robotics ecosystem, introducing four new robots, an open developer platform, and what it describes as the world’s first Three-in-One EAI Robotics Education Ecosystem Strategy aimed at accelerating consumer and educational adoption of physical AI technologies.

Leading the launch is the All-New Futurist humanoid robot, positioned as FF’s flagship professional-grade platform. Standing about 5 feet 8 inches tall and weighing approximately 121 pounds (54 kilograms), the robot is 14 percent lighter than its predecessor and features a redesigned structure for greater stability and more natural movement. Equipped with 31 degrees of freedom and a knee-joint torque of up to 320 Nm, the humanoid can run at speeds approaching 11 mph (17.7 km/h).

According to the firm’s statement, it is powered by a dual-battery system that offers up to 6 hours of runtime. It is built on a Vision-Language-Action (VLA) architecture combined with a World Model for advanced perception, reasoning, and autonomous decision-making. The robot is designed for commercial reception, guided services, industrial handling, warehouse operations, and hazardous-environment tasks. Additional specifications and pricing will be announced at Automate 2026 in Chicago on June 22.

FF also launched the FX Navi, a quadruped robot designed for home and classroom use. Marketed as the first foundational EAI learning quadruped robot in the United States, Navi combines robotics hardware with a dedicated STEM education ecosystem. The robot features 12 motorized joints, weighs 8 kilograms, and can navigate everyday indoor environments. A smartphone inserted into the head module serves as its computing platform, supporting both iOS and Android devices.

Users can customize the robot through downloadable and printable 3D head designs, while over-the-air updates will continuously add new capabilities such as autonomous following, multimodal perception, and natural interaction. Navi starts at $1,990, with optional development and curriculum packages available for students, educators, and developers.

One brain strategy

The company also previewed two upcoming humanoid robots. Master Mini, approximately one meter tall, is designed for education and robotic sports competitions, while Nova, standing about 50 centimeters tall, is intended as an entry-level educational and companion robot for children.

FF said its robotics strategy is built around a “one brain, multiple forms” approach designed to power a wide range of embodied AI robots from a shared intelligence platform. At the core of the strategy is FF’s evolving EAI Brain, based on a Vision-Language-Action (VLA) architecture and World Model, supported by an open-source developer ecosystem. The company says the platform is designed to operate across different environments, perform tasks autonomously, and adapt to new applications through its Skills and Agent system.

Rather than focusing on a single humanoid robot, FF is developing multiple robot form factors under its EAI Robot World concept, allowing a common AI brain to be deployed across different devices and use cases. The ecosystem is further supported by an EAI Data Factory that gathers real-world operational data and a global industry network aimed at improving manufacturing efficiency and reducing costs. Together, these elements form an expanding ecosystem designed to support large-scale deployment of embodied AI technologies.

FF additionally launched its open developer platform, featuring tools such as Brain Blocks visual programming, natural-language coding, robot personality customization, motion-generation software, and SDKs for professional developers. Through its new Agent Skill Store and Developer Ecosystem Program, FF aims to encourage contributions from students, educators, researchers, and software developers while expanding robotics adoption across homes, schools, and industry.

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European Space Agency (ESA) engineers are assembling one of Europe’s most advanced robotic arms, a system designed to handle samples and perform complex tasks during future missions to the Moon and Mars.

Known as the Sample Transfer Arm (STA), the robot can extend up to 2.4 meters and move with seven degrees of freedom, giving it a range of motion similar to a human arm. The system is equipped with cameras, sensors, and onboard electronics that allow it to perceive its surroundings and perform tasks with a high level of autonomy.

The robotic arm was originally developed as part of the joint NASA-ESA Mars Sample Return campaign. Its role was to transfer Martian samples collected by NASA’s Perseverance rover for eventual return to Earth.

Although the future of Mars Sample Return remains uncertain, the technology is now being positioned for a wider range of applications, including future lunar exploration missions.

Human-like space helper

The arm features a shoulder, elbow, and wrist configuration similar to a human arm. At its tip is a gripper capable of handling objects with millimeter-level precision.

The system also includes a sophisticated force and torque sensor that acts as a sense of touch. The sensor measures how an object is being pushed, pulled, or twisted in three-dimensional space, allowing the arm to manipulate items with greater accuracy.

Position sensors embedded throughout the joints continuously calculate the location of the arm’s tip, helping maintain precise control during operations.

Engineers say this level of precision could prove useful for a variety of activities in space, including collecting geological samples, transferring equipment, and supporting astronauts during surface missions.

The robotic arm also contains its own electronics unit, which acts as the system’s control center. Cameras mounted on the arm provide visual awareness, enabling the robot to detect and interact with objects in its environment.

Ready for testing

Teams at Italian aerospace company Leonardo are currently completing the integration of the system at the company’s facility in Nerviano, near Milan.

In the coming weeks, engineers will begin testing the arm in simulated space environments to evaluate its performance before any future deployment.

The project brings together companies and suppliers from across Europe. The industrial consortium is led by Leonardo and includes GMV and AVS from Spain, Maxon from Switzerland, 3DPlus from France, and COMOTI from Romania, along with suppliers from Denmark, Greece, and Germany.

The arm incorporates several specialized systems designed for spaceflight. These include a flexible wrist mechanism that allows softer contact with objects, a flat electrical harness that carries hundreds of signals throughout the arm, and structural mechanisms that help withstand the forces experienced during launch and landing.

European space agencies view advanced robotics as a key capability for future exploration missions, particularly as crews prepare for longer stays on the Moon and eventual missions to Mars.

With its ability to see, feel, and autonomously manipulate objects, the Sample Transfer Arm could become an important tool for handling samples and supporting scientific operations beyond Earth.

Chinese firm Alibaba has launched its first embodied AI model family, which links large language models with real-world robotic actions.

The Qwen-Robot suite was developed by Alibaba’s Tongyi Lab and is undergoing pilot testing with selected Alibaba Cloud enterprise clients.

The suite comprises three models focused on navigation, manipulation, and world modeling for robots operating in physical environments.

Alibaba said the models enable machines to perceive, reason, and interact with the real world, joining a growing global push to advance embodied AI beyond traditional chatbot applications.

Robots meet reasoning

Alibaba says its Qwen family of AI models has become very good at understanding the physical world. These models can recognize objects, understand spatial relationships, follow complex visual instructions, and reason about real-world environments. For example, a model can understand a command such as, “Go to the kitchen, find the red cup, pick it up, and place it on the shelf.”

However, understanding a task is different from actually performing it. While a vision-language model (VLM) can describe the steps needed to complete a task, it cannot directly control a robot’s movements. The challenge is connecting human language and visual understanding with the motor actions required to interact with the physical world.

This problem is difficult because robot training data is very different from internet data. Information collected from navigation systems, robotic arms, vehicles, and cameras comes in different formats and is expensive to gather. Simply combining all this data often creates conflicts rather than improving performance.

To address this, Alibaba developed the Qwen-Robot Suite, which includes three specialized models. Qwen-RobotNav focuses on movement and navigation. It helps robots follow instructions, navigate to locations, track targets, and support autonomous driving.

According to its website, Qwen-RobotManip focuses on physical interaction. It enables robots to grasp, move, and manipulate objects using a large training dataset collected from different robotic systems. Qwen-RobotWorld acts as a world model, predicting how environments may change and helping robots understand the likely outcomes of their actions.

Together, these models aim to enable robots to understand instructions, interact with objects, navigate environments, and make decisions in the real world.

Physical AI accelerates

Alibaba showcased Qwen-RobotNav on a Unitree Go2 quadruped powered by NVIDIA Jetson Thor hardware and a single low-resolution camera. The robot successfully navigated an unfamiliar apartment, following spoken instructions across multiple rooms without preloaded maps, while maintaining an inference latency of 196 milliseconds.

The company claims that Qwen-RobotManip, its robotic manipulation model, was trained on more than 38,000 hours of open-source data covering object handling and interaction tasks. According to Alibaba, the model recently achieved the highest score in the generalist category of the RoboChallenge real-world robotics benchmark, earning a process score of 59.83 and a task success rate of 45 percent.

The company also unveiled Qwen-RobotClaw, a robotics agent framework that enables Qwen models to use the Qwen-Robot suite as physical-world tools. In one demonstration, an agent searched for a restroom, identified an out-of-order sign, and independently rerouted to another location. Alibaba further open-sourced Chat2Robot, a browser-based platform for testing embodied AI interactions.

As competition in embodied AI intensifies worldwide, Alibaba has expanded its ambitions beyond language and multimodal software with the launch of its Qwen-Robot models. The move reflects a broader industry shift toward creating AI systems capable of understanding and interacting with the physical world.

Alibaba’s move comes as competition in physical AI accelerates globally. In the US, Google DeepMind is advancing Gemini Robotics, while Nvidia is expanding its robotics ecosystem through Cosmos, Isaac, and GR00T. Start-ups, including Physical Intelligence, Skild AI, and Figure AI, are also developing general-purpose robotic intelligence, according to the South China Morning Post.

China is strengthening its position by pairing its manufacturing advantages with growing investments in AI software for autonomous decision-making. The sector now spans AI developers, robotics firms, and EV makers. Companies such as Alibaba, Tencent, Unitree, AgiBot, UBTech, Galbot, Spirit AI, GigaAI, Xpeng, and Xiaomi are actively pursuing embodied AI technologies.

An elephant’s trunk can haul a felled log across a clearing and, moments later, peel a single banana without bruising the fruit. That range — from raw strength to delicate precision — comes from a boneless, all-muscle structure that has fascinated soft robotics researchers for years.

A new study has now mapped exactly why the trunk’s skin enables that range, down to its microscopic architecture.

Researcher Lucia Beccai and colleagues studied trunk tissue obtained from a single adult Asian elephant (Elephas maximus) that died of natural causes at Zurich Zoo, Switzerland, in 2020.

Using 35 tissue samples taken across the specimen, the team combined biomechanical testing, histology, multiple imaging techniques, and computational modeling to characterize the trunk’s skin in unprecedented detail.

A skin split into two functional zones

The central finding is that the trunk’s skin is not a uniform material — it is sharply divided into two distinct mechanical zones, each engineered for an entirely different job.

The upper skin functions like an armor-like shield: tough, protective, and 3.14 times stiffer than the skin on the underside of the trunk. That stiffness makes sense functionally — the top of the trunk is what drags across rough bark, scrapes against the ground, and absorbs impact during heavy lifting and pushing.

The underside skin is the opposite: highly flexible and acutely sensitive, built specifically for grasping. Its low stiffness allows it to conform tightly to the contours of whatever object the elephant is handling — a log, a piece of fruit, another elephant’s trunk in greeting — maximizing contact area between the skin and the object and producing a steady, secure grip without requiring excessive muscular force.

A mechanical lens hidden beneath the surface

The most striking discovery lies just beneath the surface of the underside skin: dome-shaped structures called dermal papillae. Using finite element modeling — a computational technique that simulates how physical structures respond to stress — the researchers found that these papillae function like a subsurface mechanical lens.

Rather than simply cushioning the skin, the dome-shaped papillae concentrate and amplify mechanical stress at precisely the locations where the trunk’s sensory nerves are positioned.

In effect, the structure acts as a biological signal booster — taking a faint mechanical input, such as the texture of a banana peel, and intensifying it before it reaches the nerve endings responsible for interpreting touch.

What it means for robotics

According to the study’s authors, this two-zone, mechanically tuned architecture offers a direct, ready-made blueprint for engineers designing the next generation of soft robotic systems.

The trunk does not need separate “armor” and “sensor” components bolted together — it achieves both functions through a single continuous tissue with engineered variation in stiffness and subsurface geometry.

The practical implication: by using biomimetic multi-material approaches — fabricating robotic grippers from layered materials with deliberately varied stiffness, much like the trunk’s natural upper-lower dichotomy — engineers could build grippers with passive outer protection that simultaneously amplify faint tactile signals, all without exposing delicate sensors directly to mechanical wear and abrasion.

That combination has been a persistent challenge in soft robotic gripper design: sensors sensitive enough to detect subtle touch are often the same components most vulnerable to physical damage from repeated contact.

The elephant trunk’s solution — concentrating sensitivity beneath a protective, graduated-stiffness shield — offers a structural answer that robotics researchers have been seeking for years.

The study was published in the journal PNAS Nexus.

Chinese automaker Seres has unveiled its first humanoid robot, Xiaosai, marking the company’s entry into the rapidly growing embodied AI sector. The robot is designed with visual recognition, autonomous greeting, and voice interaction capabilities.

The launch highlights the automaker’s expanding focus on robotics, with several additional embodied intelligence robots expected to debut later this year as Seres broadens its presence beyond electric vehicles.

Recently, Hyundai announced plans to deploy over 25,000 Atlas humanoid robots in the US, targeting 30,000 annual units by 2028 with locally manufactured robot components.

Industrial humanoid debut

Chinese automakers are rapidly expanding into humanoid robotics, leveraging expertise in manufacturing, artificial intelligence, supply chains, and automation to develop a new generation of embodied intelligence systems.

The latest entrant is Seres, which has unveiled its first humanoid robot, Xiaosai. The announcement was made by Seres Group director and vice president Kang Bo, who released a video showcasing the robot and confirmed that several additional embodied intelligence products are currently under development and scheduled to make their official debut later this year, reports CNEVPost.

Thought more details are not available, the humanoid is reported to be equipped with visual recognition capabilities, can autonomously greet people, and supports voice-based interaction. Its introduction highlights Seres’ broader efforts to integrate advanced robotics into manufacturing operations.

The company’s factory already employs a range of specialized robots across AI-powered quality inspection, logistics delivery, and production-line collaboration. Among them are Xiaosai 01, which performs quality inspections for chassis assembly, and Xiaosai 02, which checks the exterior configuration of completed vehicles.

According to Seres, the facility’s smart hub coordinates more than 1,600 intelligent devices, while over 3,000 industrial robots operate in synchronization throughout the plant. The factory also incorporates technologies, including the Internet of Things (IoT), big data, digital twins, 5G connectivity, and artificial intelligence, creating a highly automated digital ecosystem, reports CNEVPost.

Automakers go the robot way

Seres’ move into humanoid robotics builds on earlier investments in embodied intelligence. In October 2024, the company signed a cooperation agreement with Volcengine, a subsidiary of ByteDance, in Beijing. The partnership focuses on intelligent robot technologies, including decision-making systems, control technologies, and human-machine augmentation, with an emphasis on multimodal cloud-edge collaboration.

The launch reflects a wider trend among Chinese automakers, many of which view humanoid robots as a natural extension of capabilities developed through electric vehicle production and intelligent manufacturing. Xpeng is among the companies accelerating efforts in the sector. Earlier this month, chairman and CEO He Xiaopeng personally assumed the role of “CEO” of the company’s robotics division to speed up commercialization and development.

BYD has also confirmed that it is developing humanoid robots and is exploring the possibility of selling them through its extensive dealer network in the future. Meanwhile, Chery-backed Aimoga has moved beyond development and into commercialization. The company has begun selling humanoid robots directly to consumers, with a retail price of 285,800 yuan (approximately $42,260).

As Chinese automakers continue investing in embodied AI, humanoid robots are increasingly becoming the next frontier of competition, transforming car manufacturers into major players in the rapidly evolving robotics industry.

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Genesis AI has unveiled Eno, its first general-purpose robot, as the company pushes toward building machines that can work across factories, laboratories, hospitals, hotels and eventually homes.

The robot combines Genesis AI’s custom hardware with GENE, the company’s robotics foundation model, which serves as Eno’s control system. According to the company, the robot is designed to perform complex tasks, adapt to changing environments and complete objectives with minimal human intervention.

Genesis AI said Eno differs from traditional industrial robots because it is built to operate as a physical agent rather than execute a fixed set of commands. The company claims the system can understand goals, reason through changing conditions, retain memory and carry out multi-step tasks over extended periods.

The announcement comes as companies across the robotics sector race to develop humanoid and general-purpose robots capable of handling a wider range of real-world jobs with less specialized programming.

Built beyond humanoids

Instead of closely resembling a human, Eno uses a wheeled base topped by an adjustable tower structure. The robot can change its height and reach in real time and fold into a more compact form when not in use.

Its most human-like features are its robotic hands. Genesis AI says the hands match the form and function of human hands, allowing Eno to use existing tools and interact with environments already designed for people.

“The only path to creating a robot that can truly deliver value to society and excel in the real world is through intentional design and a single, comprehensive system,” said Zhou Xian, co-founder and CEO of Genesis AI.

“From day one we’ve approached our design and engineering through a production mindset built around bringing our hardware, software and intelligence together as a whole.”

The company said Eno was designed alongside GENE so that the robot’s body and intelligence system function as a single platform. GENE is intended to give the robot human-level dexterity and the ability to handle long-horizon tasks that require planning and adaptation.

Showing robotic intent

One of Eno’s distinguishing features is an optional built-in screen that displays the robot’s reasoning process, operational status and intended actions in real time.

Genesis AI said the feature is designed to improve transparency and help people understand what the robot is doing as it works alongside human operators.

“What Genesis is building with Eno is a fundamentally new model for extending human capability through advanced robotics,” said Eric Schmidt, former Google CEO and Genesis AI investor.

“The breakthrough is not replacing human expertise, but amplifying it — making advanced robotics genuinely useful, accessible, and scalable across industries.”

According to the company, Eno could eventually manage entire workflows rather than perform isolated actions. Examples include keeping production lines supplied, coordinating tasks across facilities and preparing workspaces for the next shift.

Genesis AI plans to begin production and targeted customer deployments before the end of 2026. Initial deployments will focus on manufacturing, logistics and laboratory environments, followed by hospitality and healthcare settings. Consumer-focused applications are expected at a later stage.

The company recently raised $105 million in seed funding and says additional robotic platforms are already under development as part of its broader roadmap for general-purpose robotics.

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An engineer has developed an open-source ceiling-mounted robotic claw designed to tackle one of the constant challenges of family life—cleaning up clutter.

Called Stringman, the system uses cables suspended from four corners of a room to move a robotic gripper across the ceiling, locating and collecting items such as toys, clothing, and other household mess.

Powered by LeRobot-based firmware, the robot can identify objects, pick them up, and transport them to designated drop-off points.

According to its maker, the project’s software, hardware designs, and build instructions are available on GitHub. At the same time, ready-to-use kits are also being offered for users who prefer not to build the system themselves.

Stringman tackles clutter

The DIY cleaning robot is designed to tackle one of the most common household challenges—clutter left behind by children and pets.

Stringman is a cable-driven robotic crane that moves across a room using high-strength lines suspended from anchors mounted in its four corners. Rather than relying on a humanoid design or a mobile robot, Stringman uses a lightweight gripper that travels through the room’s airspace, identifying and collecting objects from the floor before transporting them to designated drop-off locations.

The project was created by Nathaniel Nifong, who believes many household tasks can be automated without the complexity and cost associated with humanoid robots. According to the project’s website, reducing the number of actuators significantly lowers the overall cost of robotics hardware. Stringman relies on just four motors and a two-finger gripper with a wrist mechanism, allowing it to perform basic pick-and-place tasks while remaining relatively affordable.

The robot is built on the open-source LeRobot platform and uses imitation learning to improve its grasping capabilities. Users can train the system through teleoperation, teaching it how to pick up various household objects. The software, firmware, mechanical designs, and build instructions are publicly available under an Apache 2.0 license, allowing enthusiasts to build, modify, and contribute to the project.

To organize items, users simply designate destinations such as laundry baskets, toy bins, or trash cans using clip-on fiducial markers. Stringman’s vision system maps these locations and generates a list of objects it believes should be collected. Users can review and modify these selections through an interface or manually click objects they want the robot to remove.

Open-source robot

One of the system’s biggest advantages is its reach. Unlike robotic arms that are confined to a limited workspace, Stringman can access much of an entire room. Its cable-driven architecture enables it to move from floor-level pickups to elevated shelves without requiring rails, wheels, or articulated limbs. Because it is powered directly from a wall outlet, it also avoids the charging downtime that limits many mobile robots.

The design incorporates active swing-cancellation algorithms to stabilize the gripper as it moves. This is particularly important because the gripper hangs approximately 50 centimeters below the suspension point, allowing it to reach beneath furniture and alongside obstacles. The software continuously compensates for momentum, helping maintain accurate positioning during movement, according to its website.

Privacy has also been a focus of the project. Stringman supports a fully local operating mode in which video processing and telemetry remain within the home network. Users who prefer remote access can connect the robot to an online account and monitor or control it through a web-based interface.

Despite its promise, the system remains a work in progress. The machine-vision models still require refinement, and certain objects—particularly flat items such as books—can be difficult for the gripper to handle reliably. The suspended cables also descend into the room while the robot is operating, creating practical limitations for occupied spaces.

Even with these challenges, Stringman represents an intriguing alternative to humanoid domestic robots. By focusing on a single household task and simplifying the hardware, the project demonstrates how practical home automation may emerge from specialized, low-cost designs rather than human-shaped machines. Future developments could potentially allow the robot not only to collect clutter into bins but also to return items directly to their proper storage locations.

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ABB Robotics has partnered with California-based bionic company PSYONIC to develop more dexterous robotic systems by leveraging real-world data from human prosthetic use.

The collaboration combines PSYONIC’s Ability Hand, a touch-sensitive prosthetic hand, with ABB’s GoFa collaborative robot to study how human-generated touch and motion data can train robots to perform delicate and variable tasks.

The initiative aims to address one of industrial robotics’ biggest challenges—grasping and manipulation—while improving automation capabilities and potentially reducing engineering time for handling applications by up to 30 percent.

“Human dexterity and the instinctive understanding of how to handle different objects is one of the most difficult things to replicate in industrial-grade robotics, but it’s a fundamental need for truly autonomous and versatile robots,” said Marc Segura, President, ABB Robotics, in a statement.

Human data dexterity

ABB Robotics and PSYONIC aims to tackle one of the biggest challenges in industrial automation: robotic dexterity. The collaboration combines ABB’s GoFa collaborative robot with PSYONIC’s Ability Hand, an advanced prosthetic hand equipped with touch sensing and compliant design, to train robots using real-world manipulation data generated by human prosthetic users.

Unlike conventional robotic training methods that rely heavily on simulations, the project uses human-derived touch and motion data to teach robots how to grasp, manipulate and interact with objects more naturally. The approach is aimed at improving robots’ ability to perform delicate, variable tasks that have traditionally been difficult to automate, such as handling fragile components, adapting to changing object shapes and operating in dynamic environments.

The initiative supports ABB Robotics’ vision for Autonomous Versatile Robotics (AVR), where robots can sense, reason, move and manipulate objects with greater autonomy. It also contributes to the development of physical AI systems capable of learning from real-world interactions and applying that knowledge with industrial-grade reliability.

According to ABB, dexterity remains a major technical bottleneck in robotics, and improved handling capabilities could reduce engineering time for automation projects by up to 30 percent. The companies are exploring applications across sectors including automotive, aerospace, packaging, logistics and life sciences.

PSYONIC is working closely with ABB’s research and development team to integrate touch-enabled manipulation technologies into robotic platforms. The partnership aims to narrow the gap between human and robotic dexterity, enabling more adaptable, productive and safer automation systems for industrial environments.

Touch powered robotics

The ABB GoFa is a collaborative robot designed to work safely alongside humans in manufacturing, assembly, material handling, packaging, and laboratory environments.

It can handle payloads of up to 26 pounds (12 kilograms) and reach up to 1.62 meters, offering a combination of speed, precision, and flexibility. Built-in force and power limitation technology enables safe human-robot collaboration with minimal safety barriers, while intuitive programming tools simplify deployment. With repeatability of up to 0.02 mm, GoFa is well suited for precision tasks and improving productivity.

On the other hand, the Psyonic Ability Hand is an advanced bionic prosthetic that provides multi-touch sensory feedback, helping users experience a more natural sense of touch. Its 200-millisecond closing speed allows fast, secure grasping, while 32 grip patterns—19 pre-programmed—support activities ranging from handling small objects to typing. The hand automatically adjusts thumb positioning for a stable grip and can be configured remotely via Bluetooth-enabled iPhone and Android apps.

Powered by a 2200mAh battery, it charges through USB-C in about an hour and delivers 6–8 hours of use. Its durable, IP64-rated design resists impacts, dust, and splashes. Beyond prosthetics, organizations such as NASA, Meta, and Apptronik use it for robotics research.

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New reports suggest that Boston Dynamics’ Atlas is approaching the autonomy needed for industrial deployment.

The assessment follows recent demonstrations by Boston Dynamics that indicate significant progress in developing general intelligence.

Experts suggest advancements now enable humanoid robots to adapt to unfamiliar environments and perform a broader range of tasks beyond preprogrammed actions.

Recently, the US robotics firm launched a campaign featuring Atlas, using football to showcase human-centered robotics, AI, and human-robot interaction.

Atlas gains intelligence

Boston Dynamics is making rapid progress toward commercializing humanoid robots, with its Atlas platform approaching the level of autonomy required for industrial deployment, according to a new report from KB Securities.

Analyst Kang Sung-jin said recent demonstrations by Boston Dynamics, Hyundai Motor Group’s US-based robotics subsidiary, point to major advances in the development of humanoid robot “general intelligence” — the ability to adapt to unfamiliar environments and perform a broad range of tasks without relying solely on preprogrammed instructions, reports The Korean Herald (TKH).

A key factor behind this progress is the company’s accelerated training framework. According to the report, Boston Dynamics can run simulations equivalent to millions of hours of robot training in a single day. The resulting behaviors and skills can then be transferred to a physical Atlas robot in roughly one hour, significantly shortening development cycles and enabling faster deployment of new capabilities.

The report credits strategic collaborations with Google DeepMind and Nvidia for helping drive these advancements. DeepMind contributes expertise in artificial intelligence and learning systems, while Nvidia provides the high-performance computing infrastructure needed for large-scale simulation and robot training.

Recent Atlas demonstrations also highlight notable improvements in physical intelligence and whole-body control. In one example, the humanoid robot moved a refrigerator using coordinated movements across its entire body, while another video showed Atlas performing dynamic kicking motions that required balance, agility, and precise motor control, reports TKH.

Kang said the demonstrations reinforce Hyundai Motor Group’s commitment to industrial humanoid robotics and position Boston Dynamics as a leading contender in the emerging market. He projects the company could secure 15 percent of the global humanoid robot market by 2035 and as much as 60 percent of the premium industrial humanoid segment.

Atlas industrial leap

Boston Dynamics says one of the key advantages of its new Atlas humanoid robot is a significantly reduced “sim-to-real gap” — a common robotics challenge where skills learned in simulation fail to transfer reliably to the physical world. The company attributes this improvement to Atlas’ simplified hardware design, which makes it easier to create highly accurate simulations for training.

Unlike many humanoid robots that use numerous actuator types and complex mechanical systems, Atlas relies on just two actuator types across its body. Its arms and legs are also designed symmetrically, simplifying both control and simulation. This streamlined architecture allows engineers to train the robot more efficiently in virtual environments and rapidly transfer those capabilities to the real machine.

Boston Dynamics also redesigned Atlas to eliminate cables running across joints, enabling continuous joint rotation while reducing wear and maintenance requirements. The added flexibility allows the robot to perform movements that are difficult for conventional humanoids.

The company highlighted Atlas’s ability to handle heavy industrial tasks. Although the robot was trained using loads weighing between 50 and 70 pounds (23–32 kilograms), it successfully moved a refrigerator weighing more than 100 pounds (45 kilograms) during testing. According to Boston Dynamics, such tasks require more than visual perception, demanding full-body coordination, weight anticipation, balance, and precise force control.

The company added that Atlas’s athletic feats, including handstands and backflips, serve a practical purpose by helping develop balance, agility, slip recovery, and endurance needed for real-world industrial environments.

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A mobile robot developed by Samsung-controlled Rainbow Robotics has reportedly begun testing inside a Coupang fulfillment center, marking what could be its first deployment in a live commercial warehouse environment.

According to a report by The Korea Herald, Coupang is evaluating Rainbow Robotics’ RB-Y1 robot to determine whether it can reliably sort, transport, and handle goods within one of South Korea’s highly automated logistics networks. While neither Coupang, Samsung, nor Rainbow Robotics has publicly confirmed the pilot, industry sources told ETNews that a larger deployment could follow if the tests prove successful.

The reported trial represents an important step for the RB-Y1, which until now has primarily been used by universities, research institutions, and industrial partners for testing and development.

A different kind of humanoid

Unlike the bipedal humanoid robots grabbing headlines worldwide, the RB-Y1 takes a more practical approach. The machine combines a wheeled autonomous mobile base with a dual-arm upper body designed to mimic the movement of a human torso. 

The robot stands approximately 1.4 meters tall and weighs 131 kilograms. Its two robotic arms offer seven degrees of freedom each and can handle payloads of up to 3 kilograms per arm.

Mounted on a high-speed wheeled platform capable of traveling at 1.5 meters per second, the robot is designed to navigate warehouses, factories, and research facilities while performing tasks such as picking, sorting, and manipulating objects.

Rainbow Robotics says the platform uses a 20-axis whole-body control system to maintain stability during movement and when handling items. The company also developed collision-avoidance software that prevents the robot’s arms from interfering with each other during operation.

Why Coupang is interested

For Coupang, South Korea’s largest e-commerce company, warehouse automation has become increasingly important as the company seeks to improve efficiency and reduce operating costs.

The company has invested heavily in robotics, automation, and artificial intelligence in recent years. According to The Korea Herald, Coupang has invested more than $84 million in global AI startups since 2023 as part of broader efforts to modernize its logistics infrastructure.

The potential benefits extend beyond productivity. South Korea’s Serious Accidents Punishment Act holds executives legally accountable for fatal workplace accidents, creating additional incentives for companies to automate potentially hazardous tasks.

With a payload capacity better suited to lighter goods than heavy lifting, the RB-Y1 would likely be deployed for sorting, picking, and moving smaller items if the pilot progresses to commercial use.

Samsung’s growing robotics ambitions

The trial also highlights Samsung’s expanding ambitions in robotics. Samsung increased its stake in Rainbow Robotics to 35 percent before making the company a subsidiary, identifying robotics as one of its next major growth areas alongside artificial intelligence and advanced semiconductors.

The move comes as South Korea continues to lead the world in industrial automation. According to the International Federation of Robotics, the country has the highest robot density globally, with roughly 1,012 industrial robots for every 10,000 manufacturing workers. More than seven times the global average.

If the Coupang pilot proves successful, it could provide one of the clearest signs yet that advanced mobile manipulators are beginning to transition from research laboratories into large-scale commercial logistics operations.

A Tawniese firm has unveiled its first humanoid robot capable of interacting with the physical world.

TM Technology, known primarily for integrated circuit design, has expanded its operations into sectors such as construction, green energy, and smart manufacturing, with support from its parent, Yinglin Group.

TM Technology said the humanoid robot combines advanced hardware and software as part of a broader intelligent platform strategy, representing a significant step in its efforts to develop next-generation AI-powered robotic systems.

Recently, Techman Robot from Taiwan unveiled its TM Xplore I humanoid at Nvidia GTC 2026 in San Jose, highlighting a collaboration with QCT and Nvidia.

Embodied AI emerges

As per reports, TM Technology’s newly unveiled humanoid robot combines AI, advanced manufacturing, biomimetic engineering, and multi-modal sensing technologies to operate in complex real-world environments. Designed as an embodied AI system, the robot features a layered architecture that mirrors key functions of the human nervous system.

At its core is an AI-powered “brain” responsible for semantic understanding, reasoning, task planning, and decision-making. A dedicated “cerebellum” subsystem manages balance, posture control, locomotion, and coordinated movement, enabling the robot to perform tasks with greater stability and precision. The platform also incorporates a sophisticated perception suite that combines 3D vision systems, LiDAR sensors, and force-sensing technology to create a detailed understanding of its surroundings and support real-time interaction, reports Taiwan News (TN).

The humanoid is equipped with multi-degree-of-freedom joints and dexterous robotic hands, enabling it to manipulate objects and perform a wide range of physical tasks. According to the company, the robot can handle transportation, inspection, assembly, and other precision operations that require coordinated motion and environmental awareness. Unlike traditional industrial robots that follow pre-programmed routines, the system is designed to adapt to changing conditions through learning-based interaction and intelligent decision-making.

TM Technology said the development marks a shift from conventional hardware manufacturing toward integrated intelligent platforms. The company expects initial deployment in factories and logistics environments, where autonomous robots can assist with repetitive and labor-intensive tasks, before expanding into healthcare, caregiving, and household service applications as the technology matures, reports TN.

Taiwan’s humanoid push

Another Taiwanese firm, Techman Robot, recently unveiled its next-generation humanoid robot, TM Xplore I. The robot reflects Techman’s strategy of combining artificial intelligence with industrial automation under its “See · Think · Act – Powered by AI Robotics” framework.

Unlike traditional bipedal humanoids, TM Xplore I features a humanoid upper body mounted on a wheeled mobile base, a design intended to improve stability, mobility, and operational reliability in factory environments. The platform combines autonomous navigation with dexterous manipulation, enabling it to transport materials, interact with workers, and perform precision tasks in industries such as semiconductor manufacturing, electronics assembly, and automotive production.

The robot is powered by Nvidia’s Jetson Thor computing module, providing high-performance edge AI capabilities for real-time decision-making. It also incorporates Vision-Language-Action (VLA) technology, allowing it to process visual and language inputs simultaneously and translate them into physical actions. Techman’s software stack supports multimodal sensor fusion, generative AI inference, and autonomous operation.

TM Xplore I further leverages Nvidia’s robotics ecosystem, including Isaac Sim for simulation-based training and testing, FoundationStereo for AI-powered depth perception, and Isaac GR00T for enhanced dexterity and autonomy. Together, these technologies enable faster deployment, improved adaptability, and more efficient automation in smart manufacturing environments.

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China has reportedly deployed its first domestic AI-driven marine welding system at a smart manufacturing base in Tianjin.

Developed by Offshore Oil Engineering Co., the robotic system is designed to handle highly complex, customized, and heavy welding scenarios for offshore oil and gas platforms.

The robot is fully capable of handling specialized offshore components, including module nodes, buckle rings, and deepwater jacket strengthening rings.

20 years of life

Automotive manufacturing was long seen as the peak of robotic welding precision due to its uniform parts, but marine engineering presents a much tougher challenge. 

Marine welders handle massive, oddly shaped, and heavy components such as deepwater jacket strengthening rings and module nodes. All these tasks require a far higher level of adaptive, heavy-duty automation. This new AI-driven system aims to solve this. 

Built for extreme heavy-duty manufacturing, the system has a 20-year service life and a massive 30-ton maximum load capacity. Global Times reported that it can slice through and fuse steel up to 70 millimeters thick. Moreover, it has an overall operational efficiency exceeding 40 percent. 

Equipped with artificial intelligence, visual weld seam recognition, and intelligent 3D laser vision alignment, the mechanical arm scans its environment in real time. It maps out a custom multi-layer path-planning strategy for each unique piece of steel.

With just a single command, the robot automatically handles the entire welding process from start to finish. It manages tasks independently, using real-time data to seal the initial root pass and automatically correct its trajectory on the go.

“The robots can be activated with a single click to complete intelligent welding of product components, and are equipped with functions such as intelligent weld path correction, intelligent root pass welding, and dual-sided four-wire intelligent filling,” the company stated in an earlier release.

Plus, the AI’s precision ensures a first-pass qualification rate of over 98 percent, thereby eliminating the need for costly, time-consuming structural re-work.

AI-powered marine welder

Getting the system factory-ready was a grueling trial by fire. Before entering full active duty, the robot system underwent nearly 10 months of on-site debugging to ensure it could withstand the harsh realities of a shipyard. 

The engineering team pushed the technology to its limits, conducting roughly 1,000 rigorous welding experiments to fine-tune the AI before clearing it for the front lines of manufacturing.

Reportedly, the advanced welder isn’t working alone. An independently developed intelligent grinding robot was deployed right alongside it. 

Using cutting-edge technologies such as intelligent visual positioning and real-time force-control compensation, this autonomous companion ensures the steel is perfectly prepped and finished. 

The grinding robot has already proven its worth on the shop floor, successfully processing more than 500 steel sections while clocking a sleek 15 percent boost in operational efficiency.

It solves the issues of poor accessibility and difficult precision control, allowing the robot to execute complex spatial welds on massive structures where human operators and standard machinery struggle.

Driven by China’s expanding “AI Plus” initiative, heavy industry is rapidly moving from dangerous, labor-intensive shipyards into highly automated, self-correcting digital ecosystems. For human crews, this transformation removes them from hazardous sparks and toxic fumes, shifting their roles from manual laborers to supervisors of a faster, tireless robotic workforce.

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A Unitree G1 humanoid robot is being prepared for a planned Mount Everest expedition later this year after taking part in a high-altitude test on Mount Chimborazo in Ecuador.

The robot, named Pemba, was provided by Eastworlds Labs, the AI robotics initiative of Virtuals Protocol. It was part of a Geologic Dome expedition that placed the robot at 20,312 feet on June 5, 2026.

The Chimborazo mission is being treated as an early test before the planned Everest trek in the fall. Mount Chimborazo is known for a unique geographical fact. Its summit is the farthest point from Earth’s center and the closest point to the Sun on Earth.

Eastworlds Labs said the expedition supports its larger goal of bringing autonomous AI agents into the physical world.

The team also documented the mission and plans to create a trailer for the Everest expedition. The project is working with the production crew behind Netflix’s 14 Peaks: Nothing Is Impossible.

Everest is the next challenge

The planned Mount Everest trek will take the project into one of the world’s most difficult mountain environments.

For Eastworlds Labs and Geologic Dome, the aim is not only to place a robot in extreme terrain.

The larger goal is to test whether humanoid robots can support conservation, research, and monitoring in places that are hard or dangerous for people to reach often.

According to Eastworlds Labs, the Unitree G1 robot will be donated to the local Sherpa community for the Everest expedition. The company said this will connect the project’s legacy to the region where the next mission will take place.

The Everest plan follows the Chimborazo expedition, where Pemba was the only humanoid robot used. The Unitree G1 weighs 77 pounds (35 kg) and can fold down to 690 mm, which made it easier to transport through difficult terrain.

During the Ecuador mission, the team disassembled the robot and carried it between camps. It was then reassembled at different stages of the journey.

Chimborazo test pushed the robot

Mount Chimborazo gave the team a difficult test before Everest.

Temperatures on the mountain can fall to 5 degree Fahrenheit (-15°C), while wind gusts can reach 55 miles per hour (90 km/h). To help the robot handle these conditions, the Unitree G1 was fitted with custom cold-weather jackets, gated enclosures, and composite feet.

The robot’s autonomy system was trained in NVIDIA Isaac Sim at 1,620 times real-time speed. Eastworlds Labs said the system achieved 85 percent sim-to-real transfer on uneven terrain.

The robot was also pretrained to respond to wind turbulence and recover its balance on technical alpine terrain.

Communication during the expedition was supported by a proprietary mesh relay across camps. Satellite internet at each camp delivered 25 ms latency. This stayed below the 50 ms limit needed for live teleoperation with Reflex’s latency software.

Conservation goal behind the mission

The Everest plan is part of Geologic Dome’s broader conservation work.

The organization is building autonomous infrastructure for conservation zones. This includes communication relays, AI-powered ecological monitoring, and energy-independent robotic platforms.

Eastworlds Labs said it sponsored the Chimborazo expedition and is also contributing funding toward Geologic Dome’s nature conservation work.

Geologic Dome is currently testing its approach across three sites. These include equatorial forest in the Democratic Republic of Congo, montane cloud forests in Ecuador, and the full Himalayan altitude gradient in Nepal.

A hardware developer known online as 3DprintedLife has unveiled an open-source swarm robotics project that transforms each individual chess piece into an autonomous micro-robot capable of independent navigation, wireless communication, and self-localization.

The custom-engineered setup, appropriately named MiniBots, addresses a major challenge in miniaturization and hardware integration. To build a fully operational self-playing board using this methodology, every single piece must operate as a standalone robotic unit.

This means each individual bot requires its own processing unit, onboard power supply, dedicated motor drivers, locomotion mechanism, and positioning sensors, all while remaining small enough to fit within the standard squares of a functional chessboard layout.

Inside the Hardware of a MiniBot Piece

The physical architecture of each MiniBot piece is built around a highly compact, custom-designed printed circuit board (PCB) that fits neatly inside a 3D-printed outer shell. At the heart of each robot’s electronic stack is an ESP32-C3 microcontroller module.

Chosen for its miniature footprint and integrated wireless capabilities, this single-core module manages the primary firmware operations and low-level control loops.

For movement, each micro-robot relies on two ultra-miniature PMO8-2 stepper motors, each coupled with a specialized onboard stepper driver integrated circuit (IC).

These tiny motors drive a set of miniature wheels using direct friction engagement, allowing the piece to pivot, turn, and travel precisely across the smooth surface of the chessboard. Powering this array is a small 170 milliampere-hour (mAh) Lithium-Polymer (LiPo) battery tucked inside the base.

To coordinate the movements of up to 32 independent robots simultaneously, the system uses a dual-tier control hierarchy. A centralized command hub module acts as the master controller, utilizing the low-latency ESP-NOW wireless protocol to communicate directly with each piece.

The hub’s main board, which also operates on an ESP32-C3 wireless interface, connects to a Raspberry Pi single-board computer (SBC) via a serial link. The Raspberry Pi handles the high-level coordination and execution scripts using Python-based software.

Overcoming the self-localization bottleneck

The most complex hurdle of the swarm design is positioning. For a piece to successfully navigate to a destination square, it must first establish exactly where it is located on the 8×8 playing grid. While the developer initially considered using ultrasonic location sensors, the final design pivoted to a specialized electromagnetic tracking system.

The completed system embeds a series of scanning electromagnets directly beneath the surface of the chessboard. Concurrently, each MiniBot is outfitted with an internal magnetometer that measures local magnetic field intensity.

By utilizing precise clock synchronization via ESP-NOW, the board activates its underground electromagnets within a strict, sequential timing window.

As the electromagnets fire, the magnetometer inside the chess piece records the shifting field strengths. The onboard software takes these real-time intensity readings and runs a triangulation algorithm.

This process allows the robot to calculate its precise coordinate location relative to the active magnetic sources, effectively mirroring standard radio triangulation techniques on a micro-scale.

Prototype status and open-source potential

While a single-piece prototype has successfully demonstrated the viability of the movement and positioning algorithms, scaling the concept up to a full 32-piece swarm presents ongoing debugging challenges.

During structural testing, the developer noted that the single-core architecture of the ESP32-C3 introduced minor timing bugs in the localization firmware, which will require minor software alterations in a future update.

Additionally, implementing robust power-saving modes and designing a streamlined, multi-device recharging dock remain critical areas of focus before the swarm can play a complete, automated game.

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Researchers at Los Alamos National Laboratory have developed a new tool called the Prelim Attention Score, or PAS, to help detect when a vision-language model’s output is grounded in the image and when it may be relying too heavily on its own generated text.

The system is being viewed as a means to enhance vision-language model safety and trustworthiness.

“The PAS is a real-time, plug-and-play metric that acts as an internal monitor for the AI,” said Manish Bhattarai, a Los Alamos computer scientist.

“The system works with major existing vision-language models and requires minimal additional computational overhead, making it an efficient way to detect potential hallucinations. PAS achieves state-of-the-art accuracy in catching hallucinations, offering developers a practical path toward safer and more trustworthy multimodal AI systems,” he added.

Tracking where the model gets its answer

Most commonly used vision-language models are autoregressive. They generate each new token, or word, partly by relying on the words they have already produced.

While this process helps the model form coherent responses, it can also cause the system to lean too strongly on its own previous output rather than the image itself.

PAS monitors a vision-language model’s prediction of each token. By doing so, it helps identify where the model is drawing information from and where hallucinations are likely to occur. The tool then presents a score that alerts users to the possible presence of hallucinations in the output.

A low-overhead hallucination detector

Many autoregressive vision-language models are based on transformer architectures, a class of deep-learning neural networks that use attention patterns to weigh information as they generate an output.

The Los Alamos team studied how these models attend to the image, the text prompt and the model’s own preliminary generated words.

When PAS is integrated into a vision-language model workflow, it can run alongside the model as it handles a request.

For object mentions in the response to an image and text input, PAS computes an attention-based score showing how strongly the model relied on its own previously generated words.

The closer the PAS score is to zero, the less likely it is that the model has produced a hallucination.

“By understanding the way a vision-language model pays attention to preliminary information, PAS can help identify the exact instance where a model begins to over-rely on its own words,” said Xuan Nhat Hoang, Los Alamos intern.

“Our tool reads signals the AI is already producing, representing a low-overhead way to help ensure that information is reliable and useful,” he added.

Reliability checks for visual workflows

PAS could be used in applications where images, documents, diagrams and text are analyzed by vision-language models.

The researchers say it could eventually support reliability checks in areas such as medical imaging, scientific document analysis, engineering diagrams, remote sensing and other visual workflows where unsupported claims could affect downstream decisions.

The Los Alamos team is presenting PAS at the Computer Vision and Pattern Recognition 2026 conference, sponsored by the IEEE and Computer Vision Foundation, in Denver this month.

The work was supported by the Laboratory Directed Research and Development program at Los Alamos.

AI giant Anthropic has been forced to suspend access to its most advanced artificial intelligence models for users outside the US after the Trump administration imposed sweeping new export restrictions on the technology. The decision effectively blocks foreign governments, businesses, researchers, and individuals, as well as foreign nationals residing in the US, from using the company’s latest AI systems.

According to a US administration official, Commerce Secretary Howard Lutnick informed Anthropic CEO Dario Amodei in a letter sent on Friday that the company’s newly released Fable 5 and Mythos 5 models are now classified under export control rules.

To comply with the directive, Anthropic has moved to disable access for all affected users, reflecting Washington’s growing view that cutting-edge AI capabilities should be treated as strategically sensitive technologies.

Broad export restrictions leave no room for exceptions

Anthropic said it chose to disable access to its two newest AI models entirely because the new US restrictions apply to a wide range of users, making selective enforcement impractical. The rules cover foreign governments, companies, and individuals, and even extend to some foreign-born Anthropic employees working in the country, the Wall Street Journal reported.

“The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected,” the company said in a statement on X, noting that they were “working to restore access as soon as possible”. 

WSJ also notes that the decision marks one of the clearest signs yet that Washington is willing to intervene directly in the global AI race, treating advanced models as strategic technologies rather than ordinary commercial products. 

For companies such as Anthropic and its rivals, which are competing aggressively to release ever more capable systems ahead of highly anticipated public listings, regulatory hurdles and forced slowdowns can carry enormous financial consequences. Any disruption to product rollouts or access to international markets risks delaying growth plans and could ultimately cost the industry billions of dollars in lost revenue and valuation.

Mythos access freeze raises concerns for critical infrastructure security 

The suspension of access to Mythos could have immediate consequences far beyond the AI sector, as governments and private organizations have increasingly relied on the model to detect and fix software vulnerabilities. A prolonged shutdown could disrupt ongoing cybersecurity operations and delay critical defensive work across a range of industries, WSJ adds.

Sectors such as finance, energy, and critical infrastructure have already integrated the technology into their security workflows. The development comes only days after Anthropic introduced Fable, a next-generation model built on the same advanced architecture as Mythos, making it available to the public with additional safeguards designed to limit potentially dangerous capabilities.

Tensions between Anthropic and the Trump administration have been building for months over the rules governing how the Pentagon can deploy the company’s AI systems. At the center of the dispute are the safety guardrails and operational limits attached to Anthropic’s models. 

According to Washington DC-based AI columnist Ron Schmelzer, the biggest takeaway from the latest restrictions is that governments are increasingly treating advanced AI as a strategic technology, on par with nuclear capabilities or high-grade encryption.

“While the US was focusing on preventing foreign users from accessing Anthropic’s most powerful models, Anthropic’s reaction of making it unavailable to everyone shows that there’s a tension here between the AI mega companies efforts to rapidly increase their market share and profitability and governments efforts to make sure that AI is used for their benefit. In this push and pull, who will win, multi-billion dollar companies (or even trillion-dollar) or powerful governments?” he tells Interesting Engineering (IE).

The latest decision to impose new restrictions could further intensify that standoff, raising the stakes in an increasingly contentious debate over the military use of advanced AI.

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