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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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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