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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New research has found that humanoid robots could become effective playmates for humans, but only if their behavior aligns with people’s expectations.

A study by researchers at the Norwegian University of Science and Technology (NTNU) explored how people interact with humanoid robots during physical games.

The team conducted controlled laboratory experiments using Pepper, a social robot built to engage with humans, to better understand the experience of playing alongside or against a robot.

The findings suggest that robots can be enjoyable gaming companions when they act naturally and respond appropriately to the flow of play. However, mismatched behavior, awkward movements, or an overly competitive attitude can quickly frustrate users and reduce engagement during interactions.

Robot teammate test

Humanoid robots such as Pepper are designed to look and behave in ways that resemble humans, featuring recognizable traits such as heads, eyes, hands, facial expressions, and conversational abilities.

According to researchers, these human-like characteristics help people relate more easily to robots, but they also create expectations that robots will act in socially appropriate and predictable ways.

To better understand how people interact with robots during play, researchers conducted an experiment in which participants played a physical version of trash-can basketball with Pepper. The game involved throwing crumpled paper balls into a trash can from designated positions chosen to provide a balanced level of challenge for both the human participants and the robot.

The study examined two different gameplay modes. In one mode, participants cooperated with Pepper as teammates working toward a common goal. In the other, they competed directly against the robot. Researchers also varied the order of play, with either the human participant or Pepper taking the first turn.

By comparing these scenarios, the team evaluated how gameplay mode and turn order influenced player engagement, motivation, enjoyment of physical activity, and emotional responses.

The findings revealed that seemingly minor factors, such as who takes the first turn and whether the interaction is cooperative or competitive, can significantly affect how people perceive the experience. “Our observations show that robots can actually be good playmates, but only if they behave in ways that make sense to people,” said Yavuz Inal, associate professor at NTNU’s Department of Design in Gjøvik, in a statement.

Cooperative play wins

Participants generally reported the most positive experiences when the game began in cooperative mode. However, competitive play also proved highly engaging for many players. Several participants found competing against Pepper more exciting and motivating, particularly when they were allowed to start the game first, which increased their sense of control over the interaction.

Many players also experienced a strong sense of accomplishment when they outperformed the robot. Some even admitted that watching Pepper miss a shot added to the enjoyment. For these participants, competition with the humanoid robot created a fun challenge, clear objectives, and a rewarding sense of mastery that enhanced the overall gaming experience.

The study also found that participants became frustrated with Pepper for reasons similar to those that make people irritated with overly competitive friends or family members during board games.

Frustration was particularly noticeable when the robot started the game in competitive mode. Pepper’s slow pace, long pauses before taking a turn, and apparent determination to win often clashed with participants’ expectations of how a human-like playmate should behave. As a result, some players felt the robot lacked the responsiveness and natural flow needed for enjoyable gameplay. One participant likened the experience to playing against an “overworked printer with arms.”

According to the researchers, people expect robots cast as playmates to act like genuine players. “The results offer a glimpse into a future where robots not only assist us at work and in the healthcare system, but also participate in everyday activities such as play, exercise, and games,” said the team in a statement.

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As the 2026 FIFA World Cup begins in North America, Hyundai Motor Company has launched its biggest mobility and robotics operation for a global sporting event. The company has sent over 1,500 vehicles and advanced robots to support logistics, security, and facility management in the United States, Canada, and Mexico.

This project is a big step forward in Hyundai’s partnership with FIFA and shows how robotics and autonomous technology are becoming more common at large public events. Four custom Spot robot dogs from Boston Dynamics, which is owned by Hyundai Motor Group, are leading the effort.

Spot robot dogs head to World Cup venues

The main technology on show is four Spot robot dogs working at two key tournament locations. Hyundai confirmed the robots will be at the International Broadcast Center in Dallas and at stadiums in New York and New Jersey.

These robot dogs can navigate busy, complex spaces independently while performing different tasks. According to Hyundai, Spot will patrol, carry out real-time inspections, and help monitor areas where large crowds are expected during the tournament.

This is the first time Spot robot dogs are being used at a FIFA World Cup, making it an important milestone for Hyundai as FIFA’s Official Robotics Partner.

The company views the tournament as an opportunity to demonstrate how robots can help venue staff and security teams manage large crowds and complex event setups.

Wherever the game goes.

Hyundai is ready for the FIFA World Cup 2026 with its most advanced mobility fleet ever. From mobility solutions to SPOT, our four-legged security patrol, we're ready for kick-off.@FIFAWorldCup @BostonDynamics #Hyundai #NextStartsNow #Robotics pic.twitter.com/dUK6dZrFUq

— Hyundai Worldwide (@Hyundai_Global) June 5, 2026

Testing robotics beyond factory floors

Industrial robots have long been used in factories, where conditions are highly controlled. But event venues are much more unpredictable and challenging.

World Cup stadiums and broadcast centers will welcome thousands of visitors, staff, and media. These busy places are always changing, so robots need to move around people, obstacles, and ongoing events.

The tournament is a real-world test for autonomous technology in places where safety, reliability, and constant monitoring matter most. Experts see this as a key step in bringing robots into new roles beyond factories.

This move is part of a larger trend, as more tech companies look for practical ways to use autonomous systems in public places.

Fleet of more than 1,500 vehicles

In addition to its robotics program, the company is managing a large transportation effort to support logistics across all 16 host cities.

It is providing a fleet of 994 passenger vehicles and 506 buses. These vehicles transport teams, tournament officials, media representatives, and operational staff throughout the competition.

The fleet features a variety of Hyundai and Genesis models, so organizers can choose vehicles that meet their specific needs.

Selected vehicles are also available in hybrid versions as part of Hyundai’s broader push toward electrified mobility technologies.

Supporting the largest World Cup ever

The FIFA World Cup 2026 is expected to be the biggest edition of the tournament in history, creating unprecedented transportation and operational demands across three countries.

Managing movement between venues, training facilities, hotels, and media centers will require extensive coordination. Hyundai’s combined deployment of vehicles and robotics is intended to help address those challenges while showcasing technologies that could play larger roles in future global events.

While China dominates humanoid robotics headlines, a Korean firm showcased a humanoid learning complex motions through an open-source AI framework.

In a recent demonstration, ROBOTIS’ AI Sapiens learned the famous CORTIS REDRED Challenge motion using only smartphone video, eliminating the need for professional motion-capture systems.

The process combined video-based motion capture, motion retargeting, simulation-based reinforcement learning, and Sim2Real transfer.

According to the firm, the demonstration highlights how open-source tools can simplify humanoid robot training, enabling users to generate, learn, and execute full-body motions more easily.

Humanoid learns motion

ROBOTIS has demonstrated the capabilities of its AI Sapiens humanoid robot, an open-source platform for physical AI powered by DYNAMIXEL-Q actuators. The project is designed to make humanoid robot motion learning more accessible by using widely available hardware and open-source software tools.

In the demonstration, AI Sapiens learns and performs a complex full-body motion known as the CORTIS REDRED Challenge. Instead of relying on expensive professional motion-capture systems, the robot learns the movement from video recorded using a standard smartphone camera. This significantly reduces the cost and complexity of collecting training data for humanoid robots.

The motion-learning process begins with video-based motion capture. Human movements recorded on a smartphone are converted into digital motion data that can be processed by software. The captured motions are then passed through a motion retargeting stage, where the human movements are adapted to match the physical structure and joint limitations of the humanoid robot.

After retargeting, the robot is trained in a simulation environment using reinforcement learning. During this stage, the AI repeatedly practices the motion in a virtual world, allowing it to improve balance, coordination, and movement accuracy without risking damage to physical hardware. Simulation training also enables rapid testing and optimization before deploying the motion to the real robot.

Once training is complete, the learned behavior is transferred from simulation to the physical AI Sapiens robot through a Sim2Real pipeline. This process helps ensure that motions developed in the virtual environment can be executed successfully in the real world, despite differences between the simulation and the physical hardware.

Accessible AI robotics

ROBOTIS plans to release the motion generation and learning pipeline as open-source software, giving researchers, developers, educators, and hobbyists access to the tools used in the demonstration. The goal is to lower barriers to humanoid robotics development and enable a wider community to experiment with motion learning and physical AI systems.

According to ROBOTIS, AI Sapiens is a fully open-source humanoid robot platform designed for physical AI research and development. Standing 1.3 meters tall and weighing 34 kilograms, the robot features 23 degrees of freedom across its body, enabling a wide range of human-like movements.

The platform is powered by 23 next-generation DYNAMIXEL-Q quasi-direct-drive (QDD) actuators, including 14 QM-060 units and 9 QM-080 units. These actuators combine low gear reduction ratios, high-torque motors, and integrated control electronics to deliver high backdrivability, low impedance, and precise torque control, making them suitable for dynamic and compliant humanoid motion.

AI Sapiens is powered by an NVIDIA Jetson Orin NX 16GB computer, delivering up to 100 TOPS of AI performance for advanced robotics tasks. It supports Wi-Fi 5, Bluetooth 5.0, dual Ethernet ports, USB connectivity, and 24V/12V power outputs for connecting additional hardware.

The robot is powered by a 46.8V, 9000mAh battery. It is supported by a fully open-source ecosystem that includes hardware bills of materials, CAD files, source code, simulation assets, and development tutorials, enabling researchers and developers to customize and expand the platform.

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Researchers have developed a soft robotic gripper that can inspect fruit for ripeness and harvest it without causing damage, a capability that could help reduce food waste and improve harvesting efficiency.

The device uses soft silicone and polyurethane fingers equipped with multiple sensors to measure a fruit’s size, shape, color and firmness before deciding whether it is ready to be picked. The system was tested on strawberries and can remove the fruit by twisting its stem rather than cutting it.

The gripper was developed by a team that includes Anand Mishra, assistant professor in the Department of Mechanical, Materials and Aerospace Engineering at West Virginia University. The research began at Cornell University and is continuing at WVU.

The technology addresses a major challenge in agriculture: harvesting delicate fruits at the right stage of ripeness. Fruits such as strawberries and raspberries have a narrow harvest window and can bruise easily during picking, transportation and storage.

Gentle grip, smart sensing

“Our gripper’s quick, accurate inspections and harvesting can reduce spoilage of fruits and lower supply chain costs,” Mishra said.

“Fruit inspections are critical for harvesting decisions that have traditionally been made by human workers. However, using human workers for harvesting involves challenges such as labor shortages, health concerns and inaccuracies in picking.”

Agricultural robots have been proposed as a solution, but many existing systems are designed for controlled greenhouse environments rather than outdoor farms. Traditional robotic grippers can also damage soft fruit because of their rigid structures.

The new system combines tactile and visual sensing in a soft robotic design. Stretchable optical fibers embedded inside each finger act as tactile and curvature sensors, while a miniature camera and distance sensor are mounted in the palm.

The five-fingered gripper can identify a fruit’s shape, stiffness and ripeness while also detecting slippage during handling. Researchers reported that the device can open and close in less than two seconds, lift loads weighing up to one kilogram and achieve nearly 100 percent accuracy in shape prediction.

According to the team, the technology could be particularly useful for fruits that do not show obvious visual signs of ripeness. Avocados are one example, where growers often rely on touch rather than appearance to determine harvest timing.

Beyond the orchard

“My research group at WVU is called the Robiotics Lab because we focus on robots that mimic biology,” Mishra said.

“We design squishy, squeezy, rubbery robots inspired by animals.”

The gripper’s design draws inspiration from biological systems and resembles both a human hand and a starfish. Researchers say the soft structure allows it to absorb forces differently than conventional rigid robots, making it better suited for handling delicate objects.

Beyond agriculture, the sensing technology could find use in other fields that require careful manipulation.

“This system offers applications in space exploration, health care, food handling and underwater manipulation,” Mishra said.

“In biomedical robotics, for instance, the integration of curvature and tactile sensing could enhance wearable and rehabilitation devices.”

Researchers believe the combination of soft materials and integrated sensing could help robots interact more safely and effectively with both objects and their surroundings.

The study was published in the journal Nature Communications.

China has deployed robotic snakes to inspect power lines and help ensure a stable electricity supply.

The snake-like robots wrap around power lines and use onboard cameras and sensors to detect hazards such as damaged wires, worn components, and abnormal temperatures.

According to online media reports, the robots have inspected more than 80 miles (130 kilometers) of distribution lines serving exam sites near airport no-fly zones.

The system is three times more efficient than manual inspections, reducing the need for labor-intensive, hazardous work while improving reliability and safety.

Robotic grid guardians

Power utilities in southwestern China are using robotic snakes to inspect power lines, showcasing a new approach to infrastructure monitoring in environments where drones face limitations.

The snake-like robots have been deployed in Kunming, the capital of Yunnan province, where they move directly along power lines while inspecting critical electrical infrastructure. Equipped with cameras and sensors, the robots can identify faults such as broken wires, worn components, and abnormal temperature changes that could signal potential failures, reports the South China Morning Post (SCMP).

Developed by the Guandu District Power Supply Bureau, the robotic system has already inspected more than 80 miles (130 kilometers) of power distribution lines. According to its developers, the technology delivers inspection efficiency three times higher than traditional manual methods.

Unlike aerial drones, which are widely used for power-line inspections, the robotic snakes operate directly on the cables they monitor. This allows them to work in locations where drone flights are restricted, including areas near airport no-fly zones. The robots are also less vulnerable to high-voltage electromagnetic interference, a common challenge that can disrupt drone control systems and reduce image quality.

Meet the snake-like #robot patrolling power lines in #Kunming.

Slithering along cables, it uses onboard cameras and sensors to spot damage and overheating in real time. Even better, it draws power straight from the line, enabling autonomous 24/7 monitoring without interruption. pic.twitter.com/zXJq61oft4

— China SCIO (@chinascio) June 10, 2026

The system addresses several limitations of drone-based inspections, including restricted flight zones, weather dependence, and limited battery endurance. By providing continuous close-range monitoring of power infrastructure, the robotic snakes enable utility crews to quickly identify and address potential hazards, reports SCMP.

Intelligent grid inspectors

Chinese power utilities are deploying a range of robotic systems, including snake robots, robot dogs, drones, and surveillance cameras, to monitor electricity infrastructure and maintain reliable power supplies during the national college entrance exams.

The snake robot features a flexible, multi-jointed body that allows it to move along power lines and navigate around obstacles such as insulators. This design enables it to reach areas that are difficult for human inspectors and aerial drones to access. A camera and onboard sensors mounted in the robot’s head detect potential faults and safety risks, while a non-contact power-harvesting system allows the robot to draw energy directly from the power lines it is inspecting.

Following successful trials on multiple transmission routes, utility operators have expanded the robot’s deployment to more complex sections of the power grid. Robot dogs are also being used for inspections in high-voltage environments and other locations that are challenging or hazardous for human workers. Combined with drones and fixed monitoring systems, the robotic platforms provide continuous oversight of critical electrical infrastructure.

The deployment comes as approximately 12.9 million students sat for China’s National Higher Education Entrance Examination, known as the gaokao, one of the country’s most important academic tests. Reliable electricity is considered critical during the examination period, prompting utilities to adopt advanced robotic technologies to improve inspection efficiency and reduce operational risks, reports SCMP.

A four-legged robot dog designed to detect toxic substances could soon become the first responder sent into dangerous areas and help firefighting crews assess the damage without risking their own safety.

The remote-controlled detection robot was developed by researchers at the Graz University of Technology (TU Graz) in Austria. It is a compact, quadruped system that also incorporates measurement devices already used by fire services.

According to the research team, the detection robot has already been extensively tested by emergency services as part of large-scale pollutant drills. It has proved its ability to provide real-time data from danger zones.

Gerald Steinbauer-Wagner, PhD, an associate professor and robotics researcher at TU Graz, revealed that the dog can be deployed on missions ahead of firefighting crews. “It measures pollutant concentrations and delivers the data together with a camera image in real time from the danger zone,” Steinbauer-Wagner explained.

First into danger

In chemical accidents, emergency crews need to identify hazardous substances quickly while operating under dangerous conditions. Firefighters must typically don bulky protective suits, such as a Level A or B Hazmat suit, prior to entering contaminated areas to take measurements.

Although necessary to ensure total isolation from hazardous environments, the process can consume valuable time during an emergency. To address this issue, the team turned to robotics.

As per the researchers, the robotic dog can be sent in before firefighters to collect critical information from the scene. “This allows the emergency services to assess the situation without having to enter the danger zone themselves,” Steinbauer-Wagner continued.

The technology reduces firefighting teams’ direct exposure to toxic substances. It could significantly improve their safety during industrial accidents and hazardous materials incidents.

“Based on a comprehensive requirements analysis, we developed an appropriate robotic strategy and integrated it into existing standard operational procedures,” Steinbauer-Wagner added. “This should ensure seamless collaboration between man and machine during the operation.”

A safer perspective

Instead of creating a new system, the team made it compatible with existing fire-service procedures. The researchers conducted an extensive requirements analysis before integrating it into established hazardous materials response protocols. The robot relies on commercially available parts for mobility, sensing, data processing, communication, and visualization.

“The results of the field tests were extremely promising in terms of mobility, user-friendliness and effectiveness,” Steinbauer-Wagner reported. Meanwhile, a recent potential analysis supported by the Austrian Research Promotion Agency (FFG) highlighted several advantages of the technology.

Gerald Czech of the Austrian Federal Fire Service Association emphasized that, in the event of a real incident, he would send the robot dog into the contaminated area before the firefighters go in. “For the fire service, every minute counts during a hazardous materials operation,” he said in a statement.

Czech further said receiving measurement data and images from the danger zone while crews are still putting on protective gear could save valuable time during an emergency. “This increases the safety of the crew and improves operational command,” he concluded.

Experts from the Styrian and Upper Austrian fire service associations, TU Graz, the University of Applied Sciences Upper Austria, as well as the Disaster Competence Network Austria, support the use of detection robots. They propose making them a permanent part of fire departments’ hazardous materials response teams.

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Google has unveiled DiffusionGemma, a new experimental AI model that generates text using diffusion rather than the autoregressive approach used by most large language models today. The company says the model can deliver up to four times faster text generation on dedicated GPUs while running on consumer hardware.

The model builds on Google’s Gemma 4 family and Gemini Diffusion research. Unlike traditional language models that generate text one token at a time from left to right, DiffusionGemma creates and refines blocks of text in parallel.

According to Google, the approach enables output speeds exceeding 1,000 tokens per second on an NVIDIA H100 GPU and more than 700 tokens per second on an NVIDIA GeForce RTX 5090.

The company says DiffusionGemma is aimed at developers working on speed-sensitive applications such as interactive editing, rapid content iteration, code infilling, and other workflows where low latency is more important than maximum output quality.

Breaking text bottlenecks

Most large language models generate text sequentially, predicting one token after another. While effective, this process can leave local hardware underutilized when serving a single user.

DiffusionGemma takes a different approach. Instead of generating text word by word, it creates a 256-token block at once and then repeatedly refines it through multiple passes.

Google compares the difference to moving from a typewriter to a printing press. Rather than waiting for each token to be generated before producing the next one, the model processes an entire section of text simultaneously.

The company says this shifts the bottleneck from memory bandwidth to compute performance, allowing modern GPUs to operate more efficiently during local inference.

Another key feature is bi-directional attention. Since the model generates text in parallel, every token can attend to every other token during generation. This makes it better suited for tasks where future context matters, such as code completion, in-line editing, mathematical structures, and biological sequences.

Google highlighted a demonstration in which DiffusionGemma was fine-tuned to solve Sudoku puzzles, a task that can be challenging for conventional autoregressive models because later tokens influence earlier decisions.

Designed for local AI

The model uses a 26-billion-parameter mixture-of-experts architecture but activates only 3.8 billion parameters during inference. According to Google, this allows the model to fit within roughly 18 GB of VRAM when quantized, making it accessible on high-end consumer GPUs.

DiffusionGemma also includes an iterative self-correction mechanism. Because it evaluates an entire text block during refinement, it can identify and fix mistakes as generation progresses.

However, Google acknowledged that the model prioritizes speed over quality. The company said standard Gemma 4 models remain the preferred choice for production environments where output quality is the primary concern.

The speed advantage is also most apparent in local deployments and low-concurrency environments. In cloud settings serving large numbers of users simultaneously, conventional autoregressive models can often utilize hardware efficiently through batching, reducing the benefits of diffusion-based generation.

Google has released DiffusionGemma under an Apache 2.0 license through Hugging Face and is supporting deployment through tools including MLX, vLLM, Hugging Face Transformers, NVIDIA NeMo, and Unsloth.

Humanoid robots may soon gain more human-like dexterity thanks to a new wearable device developed by researchers at the Massachusetts Institute of Technology.

The ultrasound wristband uses high-frequency sound waves to capture the movement of muscles, tendons, and ligaments beneath the skin, creating detailed data on human hand motions.

According to the team, the data collected by the system could be used to train robots to perform household tasks by replicating the dexterous hand movements used by humans.

Last year, researchers at the University of California San Diego developed a wearable AI system that accurately interprets arm gestures and controls robots, even during running, vehicle motion, or rough ocean conditions.

Wristband teaches robots

For robots to perform delicate tasks with human-like precision, they must first understand the complex movements of the human hand. Researchers have now developed a wearable ultrasound wristband that could help bridge that gap by capturing detailed muscle and tendon activity beneath the skin and translating it into robotic motion.

Tracking hand and finger movements is a key challenge in fields such as robotics and spatial computing. Existing solutions have significant limitations. Camera-based systems can struggle with restricted viewing angles and visual obstructions, while wearable devices based on strain sensors, inertial sensors, or electromyography often limit movement or lack the resolution needed to accurately capture continuous finger motions, according to the team’s study paper.

The new wristband builds on a series of advances in wearable ultrasound technology over the past several years. Researchers have steadily expanded the capabilities of ultrasound wearables beyond gesture tracking and robotics. In 2022, scientists demonstrated a wearable ultrasound device capable of continuously monitoring multiple internal organs. Subsequent developments included wearable systems for continuous heart imaging and bladder volume monitoring.

In 2024, researchers introduced a conformal ultrasound patch capable of tracking cerebral blood flow in three dimensions and an integrated wearable ultrasound system that monitored deep tissue activity with sub-millimeter resolution using a single transducer. More recently, flexible, disposable ultrasound patches were developed to reduce manufacturing costs.

Now, in a new study, researchers led by Xuanhe Zhao at MIT introduced a wearable ultrasound imaging wristband capable of tracking arbitrary hand configurations. The device uses high-frequency sound waves to image muscles, tendons, and ligaments inside the wrist, providing a detailed view of the mechanisms that drive hand movement.

Decoding human hands

The wristband could help robots achieve human-like hand dexterity by capturing the movements of muscles and tendons beneath the skin. The device incorporates a 256-channel wireless ultrasound imaging system that uses high-frequency sound waves to monitor structures inside the wrist responsible for controlling finger and palm motions.

The ultrasound data is processed by a hybrid Transformer–ResNet AI model capable of interpreting subtle muscle activity. The system continuously tracks all 22 degrees of freedom of the human hand, covering the full range of joint movements that allow fingers and the palm to bend, rotate, and coordinate complex actions. Achieving this level of tracking has been a longstanding challenge for hand-motion sensing technologies, reported AP News.

In laboratory tests involving eight volunteers, the wristband reproduced hand gestures with high accuracy and low latency, mirroring movements within 120 milliseconds. Researchers demonstrated the system’s ability to recognize all 26 letters of American Sign Language, highlighting its precision in capturing intricate finger configurations.

Because the wristband operates wirelessly, users can control robotic systems remotely without being physically connected or located in the same room. To showcase its capabilities, the team used the technology to manipulate three-dimensional objects in virtual reality and direct a robotic hand to play the piano.

Beyond teleoperation, researchers believe the system could be used to generate large-scale datasets of human hand movements, providing valuable training data for future humanoid robots and helping them learn complex manipulation skills more autonomously.

In a landmark achievement for robotics, a bicycle-riding robot has performed the world’s first unassisted front flip, astonishing engineers and robotics enthusiasts alike. The feat, accomplished by a team at the University of Tokyo’s JSK Robotics Laboratory, marks a significant leap in the capabilities of autonomous machines and could have far-reaching implications for robotics research, mobility, and future transportation.

Engineering a robotic stunt rider

The robot, dubbed “Rider-Bot,” is built to mimic the movements and balance strategies of human cyclists. Unlike previous robotic stunts that required ramps, wires, or external supports, Rider-Bot executed the front flip entirely on its own—using only its onboard sensors, actuators, and algorithms.

The successful flip took place on a flat surface, with the robot accelerating, launching, rotating 360 degrees forward, and landing upright—all without human intervention.

According to the research team, achieving this level of acrobatic control required a combination of advanced gyroscopes, real-time feedback loops, and machine learning techniques. “This is a major step toward robots that can adapt dynamically to complex environments and perform tasks that were previously considered impossible for machines,” said Dr. Yuta Sato, lead researcher on the project.

Implications for robotics and beyond

The successful front flip demonstrates not only the mechanical resilience of Rider-Bot but also the sophistication of its AI-driven decision-making. The robot’s control system anticipates and compensates for shifts in momentum and balance, much like a skilled human cyclist would.

Researchers believe this breakthrough could inform the design of next-generation delivery robots, autonomous vehicles, and even disaster-response machines capable of navigating unpredictable terrain. “Our work shows that robots can be taught to handle extreme maneuvers, which opens up many possibilities for robust, adaptable mobility in real-world scenarios,” Dr. Sato noted.

Challenges and next steps

Despite the successful demonstration, the team acknowledges ongoing challenges. Perfecting the landing and ensuring consistency across different environments remain top priorities. The researchers plan to enhance the robot’s learning algorithms and test it in outdoor and obstacle-rich settings.

The team also hopes their achievement will inspire further research into dynamic locomotion and the integration of robotics into everyday life. “We’re just beginning to scratch the surface of what intelligent, mobile robots can do,” said Dr. Sato.

A milestone for human-robot collaboration

The Rider-Bot’s unassisted front flip not only pushes the boundaries of robotics but also highlights the growing intersection between human athleticism and machine intelligence. As robots become more capable of complex movement, their potential roles in society—from urban mobility to emergency response—are poised to expand dramatically.

With this record-breaking feat, the University of Tokyo’s robotics team has set a new benchmark for autonomous machines, paving the way for future innovations in robotic agility and intelligence.

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A new humanoid robot from China became one of the standout attractions at a major industrial exhibition after performing a Tai Chi routine.

Zoomlion’s Z01 bipedal robot demonstrated smooth, coordinated movements, highlighting its advanced motion control, dynamic balance, and human-like mobility.

The Tai Chi demonstration at KOMATEK 2026 in Istanbul showcased the robot’s ability to execute complex sequences with precision while maintaining stability throughout the performance.

In January 2026, the construction giant unveiled a full-chain AI system to drive intelligent transformation across machinery, manufacturing, management, and robotics.

Advanced balance display

While detailed specifications have not been released, Z01 is reported to feature millisecond-level motion response, long battery life, powerful onboard AI computing, and a high-degree-of-freedom design that enables smooth, natural movement. It also includes a built-in operating system that reportedly reduces robot development complexity by three to five times.

The robot’s Tai Chi routine showcased sophisticated motion control systems, coordinated full-body movement, and dynamic balance capabilities. By maintaining smooth transitions between poses and adapting its posture throughout the sequence, Z01 demonstrated the level of control required for real-world human-centered environments. Visitors closely followed the performance as the robot carried out the traditional martial art movements with remarkable consistency and coordination, according to a statement by Zoomlion.

Beyond the demonstration, Z01 represents a broader push into embodied AI and intelligent robotics. The humanoid robot is intended to support future applications that require close collaboration with people, including industrial assistance, guided services, and educational interaction. Its development reflects growing efforts to combine advanced software intelligence with robotic hardware capable of operating in dynamic environments.

“The debut of Z01 highlights Zoomlion’s growing commitment to embodied AI, with embodied AI robots and related emerging industries forming the company’s third growth curve,” said the firm in a statement.

AI robotics ecosystem

Zoomlion is advancing embodied AI through a technology stack that combines robotics hardware, intelligent software, motion control, and industrial automation. Building on years of investment in industrial IoT, the company has developed a strong digital infrastructure through Zvalley, its AI-powered industrial internet platform, supported by nearly 1,300 technical and R&D specialists.

Since 2024, the company has accelerated development of embodied intelligence systems by integrating robot hardware, core components, perception technologies, AI decision-making engines, motion-control algorithms, and software ecosystems into a unified framework. This approach enables robots to perceive their surroundings, make autonomous decisions, and execute complex physical tasks in dynamic environments.

By the end of 2025, Zoomlion had developed eight embodied AI robot prototypes spanning four categories, including humanoid and wheeled robotic platforms. These systems have been tested across real industrial settings, where they perform tasks such as logistics handling, factory inspections, loading and unloading operations, pre-assembly processes, and quality control. Operating in live production environments has allowed the company to gather valuable real-world data for training AI models and refining robot performance.

At Hannover Messe 2026, Zoomlion further demonstrated its technological progress through Robot Ops, an embodied AI development platform designed to coordinate intelligent robotic systems. The platform showcased autonomous decision-making, advanced motion control, multi-robot collaboration, and industrial deployment capabilities, highlighting the company’s efforts to transform embodied AI from research concepts into scalable industrial solutions.

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Anthropic has opened the doors to its most advanced AI system yet, but not without placing guardrails around its most sensitive capabilities.

The company on Tuesday unveiled Claude Fable 5, the first publicly accessible version of its Mythos-class AI model. Anthropic says the model outperforms every generally available system it has released to date, delivering major gains in software development, analytical work, visual understanding, and scientific reasoning.

The launch marks a significant shift for the AI firm. Until now, Mythos-level capabilities remained restricted to a small group of trusted organizations because of concerns that the technology could aid cyberattacks and other harmful activities. Fable 5 changes that equation by bringing those capabilities to a broader audience while routing certain high-risk requests through a less permissive model.

Safety before scale

Anthropic said Fable 5 includes a new layer of AI-powered classifiers designed to detect potentially dangerous prompts and jailbreak attempts. The company stress-tested those safeguards before releasing the model to the public.

If users submit requests involving cybersecurity, biology, chemistry, or model distillation, the system automatically redirects those interactions to Claude Opus 4.8 instead of Fable 5. Users receive a notification whenever that fallback occurs, allowing them to understand why the switch happened.

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available. pic.twitter.com/2AvmEjHIX8

— Claude (@claudeai) June 9, 2026

The company acknowledged that the safeguards may occasionally block harmless requests. However, Anthropic said early testing showed that fewer than 5% of sessions triggered a fallback response. “We’ve deliberately tuned the safeguards to be cautious,” the company said, adding that it plans to refine the system over time to reduce unnecessary interventions.

Built for demanding work

Anthropic argues that Fable 5 delivers its biggest advantages when tackling lengthy and complex assignments. The company says the model can stay focused across extended workflows while maintaining consistency over large amounts of context.

During early evaluations, payments company Stripe reported that the model completed a large-scale migration across a 50-million-line Ruby codebase in roughly one day. According to Anthropic, the same task would have otherwise occupied an engineering team for more than two months.

The company also highlighted gains in financial analysis. On Hebbia’s finance benchmark, Fable 5 posted the strongest results among leading models. Trading firm IMC found that the system performed well across factual analysis, reasoning tasks, root-cause investigations, and expected-value calculations.

Fable 5 also advanced Anthropic’s vision capabilities. The model can extract detailed information from scientific figures and reconstruct web applications from screenshots. In another demonstration, it completed Pokémon FireRed using a minimal vision-only setup after earlier Claude models struggled with the challenge.

Mythos stays restricted

Alongside Fable 5, Anthropic introduced Claude Mythos 5, a version of the same underlying model with fewer restrictions in select areas. Anthropic says the model offers the strongest cybersecurity capabilities it has developed so far.

Access to Mythos 5 remains limited. The company is deploying it through Project Glasswing while expanding availability to approved cyber defenders and organizations responsible for critical infrastructure. Anthropic said it intends to broaden access further through a trusted program in the future.

Fable 5 is available through Anthropic’s Claude API and consumption-based Enterprise offerings. Through June 22, Pro, Max, Team, and seat-based Enterprise subscribers can access the model at no additional cost. After that date, Anthropic plans to move users to a credit-based system before eventually restoring broader subscription access.

As competition intensifies across the AI industry, Anthropic is betting that powerful models do not have to arrive without safeguards. With Fable 5, the company is testing whether frontier capabilities and tighter controls can coexist in the hands of everyday users.

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