Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
MIT's JARVIS Challenge Explores AI's Role in Jet Engine Engineering
The JARVIS Challenge, held at MIT, investigated the potential of AI in designing and building jet engines. Over four weeks, undergraduate teams utilized AI tools to create a small gas turbine engine, aiming for a thrust of 50-100 pounds. Professor Zolti Spakovszky emphasized that while AI can enhance hardware engineering, human engineering judgment remains crucial. This initiative is significant as it highlights the evolving relationship between AI and engineering, particularly in safety-critical domains. With support from MIT Lincoln Laboratory and corporate sponsors like Safran and Voyager Technologies, students had unprecedented access to AI resources, fostering an environment of innovation and exploration. Looking ahead, the challenge showcased the importance of integrating AI into engineering workflows. As students learned to navigate AI's capabilities and limitations, it raises questions about the future of engineering education and the skills required in a rapidly changing technological landscape. No further timeline was disclosed at the time of publication.
Classes and programs Contests and academic competitions Students Undergraduate STEM education Artificial intelligence
50 Minutes and 26 Seconds! Humanoid Robot Breaks Half Marathon World Record, What Changes Will the Industry Face?
On April 19, a humanoid robot named 'Lightning' made headlines in Beijing by winning a half marathon, completing the race in an impressive 50 minutes and 26 seconds, thus breaking the previous human record. This remarkable achievement highlights the rapid advancements in robotic technology, particularly in hardware engineering and autonomous navigation. The event not only underscores the potential of robotics in athletic performance but also signifies a transformative moment for the industry, showcasing how far technology has come in mimicking human capabilities.
Humanoid Robots Marathon Technology Robotics Engineering Autonomous Navigation
DeepMind Hires Former Boston Dynamics CTO to Build the ‘Android of Robotics’
Google DeepMind has announced the appointment of Aaron Saunders as Vice President of Hardware Engineering, a significant move aimed at advancing its Gemini project into a comprehensive operating system for physical artificial intelligence. Saunders, who brings over 23 years of experience from Boston Dynamics, is expected to leverage his expertise to enhance the integration of hardware and AI technologies. This strategic decision underscores DeepMind's commitment to expanding its capabilities in the rapidly evolving field of AI, particularly in creating systems that can interact seamlessly with the physical world. The announcement comes as the company seeks to solidify its position at the forefront of AI innovation.
Google Gemini Boston Dynamics
Itera raises $12 million for fluid circuit board that rewires itself in under a minute
Deep tech startup Itera has unveiled its groundbreaking prototype of the world's first fluid circuit board, a technology that enables engineers to rewire and retest physical electronic circuits in under a minute. This significant advancement aims to streamline the design and testing processes in electronics, potentially transforming how engineers approach circuit development. The announcement was made as Itera emerged from stealth mode, highlighting its innovative capabilities. To support its launch and further development, the company secured $12 million in seed funding from notable investors including Upfront Ventures, Costanoa Ventures, and Colle Capital. This funding will facilitate Itera's efforts to bring its revolutionary technology to market, marking a pivotal moment in the evolution of electronic circuit design.
Electronics News automation news deep tech startups electronic design automation electronics design
SpaceX's Starmind Faces Feasibility Challenges for 1 Million Satellite Deployment
On January 30, 2026, SpaceX submitted a request to the FCC to launch up to 1 million satellites as part of its Starmind orbital compute constellation. This ambitious plan is unprecedented, as the total number of satellites ever launched globally is in the low tens of thousands. The proposal seeks a waiver from standard deployment milestones, citing reliance on the Starship's full reusability for success. The significance of this request lies in the technical and logistical challenges it presents. Experts warn that low Earth orbit may not support the proposed number of active satellites without risking a debris cascade. SpaceX's own IPO prospectus acknowledges unresolved dependencies related to Starship's launch cadence and reusability, which are critical for the orbital AI compute strategy. Looking ahead, the timeline for achieving the necessary launch cadence and manufacturing capacity remains uncertain. SpaceX's Gigasat facility in Texas aims for volume production by late 2027, but this would require unprecedented output levels. No further timeline was disclosed at the time of publication, leaving the feasibility of the Starmind project in question.
Better Hardware Could Turn Zeros into AI Heroes
Researchers at Stanford University have developed a groundbreaking hardware accelerator named Onyx, designed to enhance the efficiency of artificial intelligence (AI) computations by leveraging the concept of sparsity. This innovation comes in response to the growing energy demands and carbon footprint associated with increasingly large language models (LLMs), such as Meta's recent Llama release, which boasts 2 trillion parameters. Onyx aims to address the limitations of current hardware, which often fails to fully utilize the sparse nature of AI models, where many parameters are effectively zero. By re-engineering the architecture to support both sparse and dense computations, Onyx achieves significant energy savings—consuming up to one-seventieth the energy of traditional CPUs and performing computations eight times faster on average. The development of Onyx reflects a broader trend in AI research, where experts are exploring new algorithms and hardware solutions to mitigate the environmental impact of AI technologies. The team at Stanford plans to expand Onyx's capabilities to support a wider range of AI operations, potentially revolutionizing the field and paving the way for more sustainable AI practices. As the demand for efficient AI solutions grows, Onyx represents a promising step toward balancing performance and energy consumption in machine learning.
Ai-models Gpus Energy-efficiency Data-compression
Tesla Optimus Hardware: Actuators, Hands & Sensors (2026)
In March 2026, a comprehensive technical guide detailing the advanced hardware of the Optimus robot was released, highlighting its sophisticated actuator types and the inclusion of 50-actuator hands. The guide also emphasizes the robot's power capabilities, featuring a robust 2.3 kWh battery that supports its functionality. Additionally, it outlines the robot's degrees of freedom (DoF), sensor integration, and the utilization of a full self-driving (FSD) chip, showcasing the cutting-edge technology that underpins Optimus. This release aims to provide insights into the engineering and design elements that contribute to the robot's operational efficiency and versatility, reflecting ongoing advancements in robotics and artificial intelligence.
Confirmed: Unitree H2 Specs Reveal a 31-DOF Humanoid with a Major Hardware Overhaul
The recently unveiled Unitree H2 has garnered attention for its advanced specifications and innovative design, showcasing a total of 31 degrees of freedom. This includes a three-degree-of-freedom (3-DOF) serial-stack waist, a remotely actuated 'quasi-serial' ankle configuration, and a two-degree-of-freedom (2-DOF) neck. This re-engineering marks a significant evolution from its predecessor, the H1. The official details and expert evaluations highlight the H2's enhanced capabilities, positioning it as a notable advancement in robotic technology.
H1 Unitree Robotics IROS 2025 H2
Re:Build Manufacturing Launches US-Made Lithium-Ion Battery Packs for UAVs
Re:Build Manufacturing has initiated direct commercial sales of its new lithium-ion battery packs designed for unmanned aerial vehicles (UAVs). This product line includes the Core, Power, and Performance series, which cater to various energy configuration needs. The launch is part of a broader manufacturing expansion at the company's Pennsylvania facility, aimed at producing Group 1, Group 2, and Group 3 unmanned aircraft systems (UAS). The introduction of these battery packs is significant as it addresses supply chain vulnerabilities in domestic aerospace hardware. UAV developers, particularly in dual-use, public safety, and military sectors, face stringent procurement regulations regarding component origins. Re:Build's assembly protocol utilizes non-FEOC battery cells to ensure compliance with National Defense Authorization Act (NDAA) procurement frameworks, thus enhancing the reliability of UAV operations. Looking ahead, Re:Build Manufacturing is set to provide dedicated engineering support for custom energy storage solutions, including Battery Management System (BMS) development. The company's advanced manufacturing facility in New Kensington, spanning 175,000 square feet, is designed to facilitate the transition from UAV prototyping to mass production, addressing common scaling challenges in the industry. No further timeline was disclosed at the time of publication.
Military
LA High School Students Engage with Real Robotics at Faraday Future Headquarters This Summer
A group of K-12 students in Los Angeles has been hands-on with real humanoid robots and industrial-grade robotic dogs at Faraday Future's headquarters this summer. On July 15, Faraday Future announced that its EAI Robotics Summer Camp, in collaboration with the Lynwood and El Segundo school districts, has entered its second week, alongside a partnership with Triple I, a full-cycle education organization in the U.S. The summer camp is notable for using actual robotics equipment rather than toy kits or computer simulators. Students have worked with Faraday Future's own robots, including the Navi, an educational four-legged robot priced under $2,000, the industrial-grade Aegis, and the humanoid robot Master. The camp employs a five-day progressive learning structure, culminating in students programming and debugging real hardware. Participants have transformed from beginners to capable of autonomous system demonstrations within just one week. Faraday Future's Co-CEO Chen Zhe emphasized the importance of immersive engineering experiences for students and how their feedback aids product iteration and course design. He believes education will be a key application area for scaling consumer robotics in its early stages, as Faraday Future aims to bridge classroom learning with practical experience and home education.
Robotics Education Hands-on Learning Consumer Robotics Programming STEM
Xuri S600 Achieves Mass Production Validation with Over 20 Clients at WAIC
On July 15, during the World Artificial Intelligence Conference (WAIC) in Shanghai, Digua Robotics announced significant progress regarding the Xuri S600. The company has secured partnerships with over 20 leading clients, collaborating with more than 100 industry partners to complete adaptive integration across various sectors, including humanoid robots and industrial applications. Several collaborative models have entered real-world testing and mass production validation phases, accelerating the platform's industrial deployment. The importance of this development lies in the Xuri S600's recognition for its core capabilities in embodied intelligence mass production. The platform's single-chip full-stack design offers high reliability and a comprehensive engineering toolchain, which has garnered industry-wide acknowledgment since its launch in November 2025. Notably, multiple benchmark projects have made clear progress, with the A3 robot from Itstone Technology set to deploy the Xuri S600 for large-scale industrial applications. Looking ahead, the Xuri S600 is transitioning from technical validation to real hardware and mass production verification. Key clients have shared their experiences, emphasizing the platform's ability to facilitate stable and predictable robot deployment. No further timeline was disclosed at the time of publication.
Embodied Intelligence Humanoid Robotics Industrial Automation AI Technology
Inside Momenta: Elon Musk-style CEO, AI obsession, and mass production machines.
Momenta, a Chinese autonomous driving company, has made significant strides in the industry, achieving a market capitalization of HKD 70 billion on its IPO debut on July 8. Founded by Cao Xudong, who frequently travels to the U.S. to experience Tesla's Full Self-Driving (FSD) technology, the company boasts over 50% market share in designated vehicle models and over 60% in mass-produced vehicles. Cao's strategic decision to focus on L2-level mass production, rather than the more commonly pursued L4 direction, has been pivotal in establishing Momenta's competitive edge. Despite initial skepticism and funding challenges, his commitment to engineering efficiency has driven the company to achieve a gross margin increase from 17.5% to 71.6% over three years, alongside a doubling of revenue and narrowing losses. As the market for L2 technology becomes increasingly crowded with competitors like Huawei and Horizon Robotics, Momenta faces mounting pressure. The company is also navigating rapid advancements in AI technology, with a focus on world models and robotics as future growth areas. Cao's vision includes integrating hardware and software to enhance cost-effectiveness and maintain a competitive advantage. Despite the challenges, Cao remains optimistic about Momenta's potential, emphasizing the importance of a passionate team dedicated to AI innovation. As the industry evolves, the company's past successes may serve as a foundation for navigating future uncertainties.
SpaceX Launches Starmind Project for 1 Million AI Satellites by 2028
SpaceX has officially named its orbital AI infrastructure project 'Starmind,' which aims to deploy a constellation of up to 1 million satellites. This initiative, confirmed by Elon Musk on June 22, 2026, will enable AI inference directly in space, utilizing solar energy rather than terrestrial power sources. The first satellite, designated AI1, was unveiled on June 8, 2026, and is designed to operate in sun-synchronous orbits. The significance of Starmind lies in its potential to overcome the limitations faced by ground-based data centers, such as land, power, and water constraints. By running AI computations in orbit, Starmind can provide a more efficient solution to the growing demand for AI computing power. The project leverages the existing Starlink infrastructure for data transmission, distinguishing its function from Starlink's internet relay capabilities. Looking ahead, SpaceX plans to begin hardware deployment with the AI1 satellite, while full-scale production and deployment of the satellite constellation are targeted for 2028. As of now, no Starmind satellites have been launched, and further engineering challenges remain to be addressed, particularly regarding the scalability of the satellite design.
Small-AI Models Gain Traction Around the World
One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year.The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says.The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. “Our server was in the United States, and just to get the result of a single scan was taking me over 5 minutes.”So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo.More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of “small AI.”Small AI for Global Health Care AccessSmall AI is a far cry from wealthy nations’ colossal large language models (LLMs), hyperscale data centers, multibillion-dollar investments, and debates about AI consciousness. But for millions of people around the world, the only AI that matters, and often the only kind available, is small. (According to a World Bank Report issued in November, only 0.7 percent of internet users in the world’s poorest countries have used ChatGPT, compared to a quarter of all internet users in the most developed nations.)“Most people are discussing AI from the LLM/generative side. But that needs a lot of computing power, electricity, massive data, and skilled people to manage it,” Ajay Banga, president of the World Bank, said last January at the World Economic Forum, in Davos. “Outside the developed world, other than maybe India and China, very few countries have that combination.”By contrast, small AI can deliver useful, even life-saving services to people in areas that have none of those things, Banga said. In India, where the government’s AI plans call for more development of small AI, many such systems are working for farmers.For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.“This is the most important area in AI nowadays,” says Marcelo José Rovai, a professor at the Institute of Engineering and Information Systems at the Federal University of Itajubá, in Brazil, who was involved in all three projects. “It’s growing very fast.”Low-Power, Small-AI Models on Devices Small AI models can run on a variety of low-power devices, including [from left to right] an Arduino Nano 33 BLE Sense, a Seeed Wio Terminal, and an Arduino Portenta.Moez AltayebFor Alonge, Rovai, and other advocates, small AI is not just “a promising trend,” as that November World Bank report calls it. It may be, in the long term, the form of AI that will touch the most lives and remain sustainable after some of the giant models become too costly for most users.“I think the future of AI is not like one giant model, at a center. I think it’s millions of small, precise models deployed at the edge, each one solving like a specific problem, a specific context,” Alonge says. This is partly because much of humanity—including people in parts of rich countries as well as the developing world—lives without access to cutting-edge frontier models. But, he says, it’s also because those models are not sustainable.“If someone is not subsidizing it, most people will not be able to afford those models. So those of us who are said to be small-AI developers are the ones who will have to build for the majority of the world,” Alonge says.There is no strict definition of “small AI,” but people often use the term for language models with at most a few billion parameters. (Compare that to cutting-edge models, which can include more than a trillion.) That’s small enough to run directly on a phone or a Raspberry Pi. That’s what allows these applications to run on devices without a connection to a data center and use only a few watts of power, often supplied by a battery or a solar panel.Despite their small footprint, these models aren’t fundamentally different technology from that of gigantic AI models, Rovai says. Many instances of small language models were created the same way the phone-based version of Alonge’s pharmaceuticals scanner was—by “pruning” large models, or removing the parameters that weren’t involved in the task. The result is a system that’s less capable generally but still very good at the specific job it was pruned for, Rovai says. A lighter version of RxAll’s RxScanner spectrometer sends its results to an AI model run locally on a phone to check that a drug’s molecular signature is genuine.RxAllOther small models are created by “distillation.” They are trained to mimic a large model, until their performance approaches that of their “teacher,” Rovai says. In other cases, a larger model’s precision is reduced, for example, so that a model run on 32-bit architecture can run on 8-bit designs. In situations where the machine learning application is being used to classify data or predict patterns (like an ant infestation), it’s trained from the beginning on a small device, not derived from a larger model at all. Running all these small, specialized systems is becoming easier, Rovai says, for two reasons.The first reason is that hardware is getting better and more capable while using less power, he says. This means more and more phones can run small AI—especially those equipped with neural processing units, which are specialized chips that handle AI tasks like facial recognition and changing the brightness, shadows, or contrast in a photo.In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.The second reason Rovai cites is the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.Rovai illustrated these reasons on a Zoom call, using one of his most recent experiments. Holding up a device, he says, “This is the new Arduino UNO Q—a US $50 device with a Qualcomm chipset. I’m running a language model here, which collects data from sensors and analyzes that data to detect tiny pools of water where mosquitoes might be breeding. It takes 3 watts to run it.”Support for Small-AI DevelopmentConvinced that millions of people are already benefiting from these kinds of applications, the World Bank now actively promotes small AI with grants, mentorship programs, financing, technical advice, and models of government policies that are friendly for small-AI development. For example, in Rwanda, the World Bank is backing a government program to help low-income households get devices that can run AI.All that said, no one claims that large language models are going away entirely. To create a generative AI that can run on a phone or other small device requires the architectural insights, data processing, and results of a larger model, Rovai says. “We need the big models to create these smaller models.” And for all that small AI can benefit people without access to big AI, the technology can’t solve the larger problems of development and digital inequality, Alonge says. Implementing small AI won’t allow nations to escape the challenge of creating an ecosystem to support AI: reliable power, a supply chain that works, and an educational system that develops the talents needed to create AI tools.Though his drug-scanning system can run for days on a phone with no connection, “you still want to be able to enable periodic syncing for updates with new signatures for the medications and analytics,” Alonge says. “And even when you are using batteries, reliable power is important. That phone battery is not going to last forever.”In many parts of the world, the future of small AI isn’t assured, he says. “It works, and many places will eventually need to use it. The question is whether or not the political actors are wise enough to invest in infrastructure to support it long term.”
Small-language-models Artificial-intelligence Llms
Japan Pioneered Humanoid Robots—Can It Now Catch China?
“In the future, the relationship between humans and robots will deepen, and the distinction between them will probably disappear.” This prediction, from one of the attendees at the recent Humanoids Summit in Tokyo, might have been unremarkable had it not come directly from an android that was first introduced to the world 20 years ago. Geminoid HI-6 is the sixth-generation of a robot originally designed in 2006. The mechanical twin of Osaka University professor Hiroshi Ishiguro, Geminoid HI-6 is now equipped with a large language model trained on Ishiguro’s own writings and interviews. It has advanced conversational skills and can even have a chat with its creator, an eerie spectacle. But at the Humanoids Summit, Geminoid was one of the few humanoid robots from Japan, the country that pioneered the form factor.While the event in Tokyo only had about 40 robots on display, Chinese systems outnumbered Japanese by roughly three to one. Some Japanese robotics firms were even using Chinese robots in their own technology demonstrations, something that would have been unthinkable in the recent past—one Japanese engineer described the situation as “sad.” The conference was a stark reminder of how Japan has ceded its early lead in humanoid robot development to overseas competitors, and the challenge it now faces to secure a place in an ecosystem increasingly dominated by general-purpose robots powered by AI. Twenty-five years ago, Japan was turning out groundbreaking humanoids that were showstopping in their abilities, but they were not commercialized as practical machines in any meaningful way. Heavily influenced by science fiction and lacking practical applications, they were mostly expensive technology demonstrations that were eventually mothballed. What Japan retains, however, is robotics design and know-how, which it must leverage to be a key player in the rapidly evolving humanoid ecosystem. Learning to Walk—Then Standing StillTo anyone who has seen recent videos of Chinese humanoids doing kung-fu and synchronized acrobatics, as well as half-marathon races, China’s remarkable progress in the field is nothing new. At the Humanoids Summit, Toyota showed a video of its latest basketball-playing robot, and Honda exhibited its latest robot hand, but the full-scale humanoids on the floor were mostly Chinese–the kid-size K1 machines from Booster Robotics of Beijing were dancing to Michael Jackson tunes. The full-scale G1 humanoid from Unitree Robotics of Hangzhou was also doing demos. “You cannot sell these bipedal systems in Japan for safety and compliance reasons,” says Shuichi Nagao, a frequent visitor to China as CTO of Omakase Robotics, a division of Zeals, a Japanese humanoid robot developer. Omakase was exhibiting a G1 modified with an external PC controller, a dextrous hand, a suction-cup manipulator and a sensor “hat” with an extra speaker, mic and camera. “In China, the government is pushing humanoid development. They didn’t have an industry 20 years ago. The people pushing it are young, in their 20s and 30s. It’s a really different mentality out there,” says Nagao. “Big players in Japan are still looking for use cases for humanoids. In China, they’re already doing mass production and reducing the cost, so other countries can’t compete with them anymore.”Another Japanese company showing off G1 bots was summit sponsor GMO AI & Robotics, a subsidiary of Japanese internet company GMO. It’s using the robots in partnership with Japan Airlines to load and unload cargo containers at Tokyo’s Haneda airport. The cargo project is a trial—like many other humanoid experiments—but the fact that Chinese machines have penetrated so far into Japan’s ecosystem upends a long history. In 1973, scientists at Waseda University in Tokyo built WABOT-1, considered the first full-scale humanoid robot and capable of slow bipedal locomotion, grasping objects and simple communication. It inspired Honda’s groundbreaking Asimo humanoid, but it was never commercialized. Asimo was eventually retired in 2022, the year ChatGPT was released. Two years later, Unitree’s G1 went on sale for US $16,000. China’s High Torque Technology Co. showed off its Mini Pi biped, customized with an anime-inspired head, at Humanoids Summit in Tokyo. The regular version is priced at $3,500. Tim HornyakSupply and DemandJapan’s development of humanoids happened before practical applications or widespread demand were in place, but bad timing is only part of the story—Japan also has a history of developing technologies that might appeal to domestic consumers but not necessarily those overseas. For example, decades after they first appeared, its highly engineered, multifunction toilets have only recently found a following abroad. Japan’s humanoid prowess was partly built on the back of its legendary industrial automation, yet even that stronghold has eroded. Ani Kelkar, a partner from McKinsey & Company in Boston who produces analytical reports about the robotics industry, told the summit audience that while Japan occupied the top spot in the world in manufacturing robot density (the number of multipurpose industrial robots in operation per 10,000 employees) from at least 1994 to 2009, it then slipped to second in 2014, third in 2019 and fifth in 2024. In that year, South Korea was at the top of the leaderboard with a robot density of 1,220 compared to Japan’s 446. The International Federation of Robotics estimates China now has the most operational industrial robots in the world, with around 2 million total units, approximately 4.5 times more than Japan. “The annual installation numbers are impressive too: 54 percent of all robots installed worldwide in 2024 were deployed in China,” the IFR said in a release in April 2026. “I think the loss of Japanese leadership is more to do with the rise of China as a manufacturing powerhouse including for sectors that Japan had high export levels,” Kelkar said in an email interview. “The recovery has not yet happened as Japan ‘missed’ the rapid acceleration in AI for robotics and is now playing catchup.”How Japan Can Adapt Kelkar believes Japan has a US $100 billion opportunity in general-purpose robotics, which are machines that can perform a wide variety of tasks, and it cannot rely on the slower-growing industrial robot market, which is centered on factory machines that do one simple and predictable task like welding car parts. He points to a McKinsey white paper suggesting that while Japan has much of the hardware and technology experience needed to support general purpose robot development, it must change its strategy to capture more share in AI, software, data collection and robotics platforms.Tetsuya Ogata is a professor of engineering and director of the Institute for AI and Robotics at Waseda University, the birthplace of humanoids in Japan. He briefed the summit on how a nonprofit he chairs, the AI Robot Association (AIRoA), is working with Toyota and other members to develop foundational technologies for collaborative use. For instance, AIRoA has collected some 80,000 hours of data on remote operation of mobile manipulators, and Ogata believes it’s the largest dataset of its kind. Using the data, it built and verified Vision-Language-Action (VLA) models, and it has also started data collection for dual-arm mobile manipulation. In an interview, Ogata acknowledged Japan’s struggle to find its place in the changing landscape. “The world of AI is inherently a game of scale,” says Ogata. “Therefore, Japan’s absolute prerequisite is to secure a competitive baseline of scale—in data, computing resources, and talent. Beyond that, what I consider most critical is a mindset shift: rather than trying to hoard scale within a single nation or company, we must grow stronger by collaborating with a diverse ecosystem of domestic and international players.” Specifically, this means creating a ‘collaborative domain’ to address data—the single biggest bottleneck—through industry-wide cooperation rather than data-siloing. By collectively nurturing a pre-competitive, shared data infrastructure and foundation model, individual companies can then compete on top of it with their own applications. “By offering this open ‘data ecosystem’ to the world, we can engage global players and establish a ‘third pole’ alongside the US and China,” says Ogata. “I believe this is how Japan can reclaim its global presence.”In 1999, Japan introduced the world’s first mobile internet services platform. But being first didn’t turn Japan into a smartphone manufacturing or design center—it’s now merely a supplier of parts to other countries who are leading the smartphone industry. If Japan can avoid a repeat of that experience and successfully deregulate, diversity, and commercialize its original humanoid dreams, it stands a better chance of influencing the direction of the industry and reaping billions in value. As automobiles and electronics were pillars of Japan’s industrial strategy in the last century, Japan could make humanoid robots one of its key value generators in the 21st century, an approach that would not only deliver economic benefits but give Japan greater clout in how the industry will evolve. Just like Japanese cars, electronics, and even toilets, Japanese humanoids could stand for craftsmanship and reliability. It’s a legacy that Japan can’t afford to give up.
Japan Robotics Humanoids Humanoid-robots
Transforming Smart Models into Productive Robots: The Journey of Self-Variables
Self-Variables has made significant strides in robotics by evolving from advanced models to practical applications, showcasing their self-developed technologies and engineering expertise. This transition, achieved through overcoming challenges in training, hardware development, and real-world implementation, has enabled the company to demonstrate its capabilities across various sectors, including home cleaning and logistics. The successful application of their robots has garnered substantial investment from industry leaders, highlighting the growing interest in their innovative solutions.
Robotics AI Automation Machine Learning
Greensea IQ is Awarded $18 Million Contract by the US Navy for the Bayonet Underwater Controller (BUC) Development and Sustainment
Greensea Systems, Inc., operating as Greensea IQ, has secured an $18.15 million Indefinite-Delivery/Indefinite-Quantity (IDIQ) contract to deliver hardware, software, and engineering technical services. This contract, awarded for an undisclosed duration, focuses on the development of underwater controllers essential for the operation of autonomous and remotely operated systems in maritime settings. The contract aims to enhance capabilities in underwater technology, reflecting the growing demand for advanced solutions in marine environments.
greensea iq u.s. navy new contract bayonet underwater controller (buc) development and sustainment
Kawasaki Robotics and Dexterity Expand Collaboration to Scale Physical AI for Warehouse Logistics
Kawasaki Robotics has unveiled the RL030N, an advanced 8 DoF (degrees of freedom) robot arm platform that integrates cutting-edge industrial robot engineering with Dexterity's Mech hardware and Foresight World Model technology. This innovative platform aims to enhance automation capabilities across various industries by providing greater flexibility and precision in robotic tasks. The announcement was made during a recent technology expo held in Tokyo, showcasing the latest advancements in robotics and automation. By combining expertise from multiple sectors, the RL030N is designed to meet the growing demand for sophisticated robotic solutions that can adapt to complex environments and tasks. The collaboration between these industry leaders highlights a commitment to pushing the boundaries of what robotic systems can achieve, ultimately aiming to improve efficiency and productivity in manufacturing and other applications.
OnLogic to Showcase Practical Physical AI and Full-Facility Workload Consolidation at Automate 2026
OnLogic, a leader in industrial computing, is set to showcase its innovative edge solutions at the Automate 2026 show, taking place from June 22-25 at McCormick Place in Chicago, Illinois. Positioned at Booth 867, the company aims to highlight practical applications of Physical AI and advanced workload consolidation, moving beyond the prevalent cloud AI hype. As industries increasingly seek to implement artificial intelligence, many face challenges such as cloud latency and high bandwidth costs. OnLogic's VP of Product Engineering, Sheldon Sun, emphasizes the need for reliable edge computing to enable real-time operations in demanding environments. The company will demonstrate how its robust hardware can effectively bridge the gap between sophisticated software and the realities of modern industrial settings. Attendees can expect live demonstrations, including a forklift near-miss detection system powered by edge computing and an automated quality control inspection using advanced machine vision. OnLogic will also present a range of industrial and edge computing solutions, including the Helix 520 Series and the ultra-compact CL260 fanless computer, designed for various applications within the industrial sector. For more information on OnLogic's offerings and to engage with their team during the event, visitors are encouraged to stop by Booth 867 or visit their website.
How JPL Keeps the 13-Year-Old Curiosity Rover Doing Science
The Curiosity rover, which has been exploring Mars for 13 years, continues to operate effectively despite the challenges of its hostile environment. Since its successful landing in August 2012 at the Jet Propulsion Laboratory (JPL) in Pasadena, California, Curiosity has traveled nearly 37 kilometers, drilled into 42 rocks, and captured approximately 763,000 images. JPL engineers, including assistant team chief Alexandra Holloway, have implemented ongoing software updates and innovative solutions to keep the rover functional, even as it faces wear and diminishing power. Holloway highlighted the rover's longevity, attributing it to robust engineering and continuous maintenance efforts. While Curiosity and the younger Perseverance rover share similar hardware, Perseverance features additional capabilities for autonomous navigation, reflecting their distinct mission objectives. Curiosity's operational challenges include wheel wear from sharp rocks and power consumption from its nuclear source, which decreases over time. Engineers have developed strategies to optimize power usage, such as reducing computer activation time and parallel processing tasks. Looking ahead, Holloway noted that while Curiosity's arm may eventually fail, the rover still possesses valuable remote sensing instruments that will contribute to future Mars exploration. With its power source expected to remain viable through at least 2035, Curiosity's mission continues to yield significant scientific insights, paving the way for future missions.
Curiosity-rover Mars Jpl
Hellbender Secures $12.5M Seed Round to Accelerate Domestic Manufacturing of Physical AI and Launch Its On-Edge Camera Line
A robotics engineering and hardware firm has positioned itself as a key player in the realm of Physical AI by developing a foundational platform that enhances on-edge computer vision capabilities for autonomous systems. This innovative approach utilizes a robust portfolio of intellectual property to advance the field of robotics. The firm aims to revolutionize how autonomous systems perceive and interact with their environments, thereby improving their functionality and efficiency. By harnessing cutting-edge technology and data-driven insights, the company is set to make significant strides in the integration of AI within robotics, paving the way for more sophisticated and capable autonomous solutions.
6-Axis Robot Arms Explained: Kinematics and Terminology
JAKA, a leader in robotics, has unveiled its advanced 6-axis robot arm, the JAKA Zu, designed to enhance automation in modern manufacturing environments. This innovative system features interconnected joints that provide versatile movement and precise control in three-dimensional space, enabling it to perform complex tasks such as assembly, welding, and material handling. The robot's compact structure and lightweight design make it particularly suitable for factories with limited space, allowing it to efficiently manage multiple production lines and significantly reduce operational costs. The JAKA Zu excels in palletizing operations, automating tasks that traditionally required manual labor. By accurately stacking items across various production lines, the robot not only improves workflow and reduces operator fatigue but also enhances workplace safety. This automation leads to consistent throughput, freeing human workers from repetitive and ergonomically challenging tasks. JAKA emphasizes the importance of understanding kinematics and the terminology associated with the robot's joints to maximize productivity and minimize setup errors. The integration of hardware and software in the JAKA Zu ensures smooth operation and low maintenance, while its modular design allows for quick adjustments to accommodate different tasks without the need for specialized engineering support. Through these advancements, JAKA aims to optimize production schedules and maintain high-quality standards, ultimately creating a safer and more efficient working environment for operators.
From Design to Deployment: The Complete Lifecycle of a Cobot Manufacturer's Product
JAKA Robotics is redefining the lifecycle of collaborative robots, emphasizing a user-centric design and rigorous engineering processes. The journey begins with a foundational design phase, where the company focuses on creating cobots that balance strength, precision, and safety. This involves intensive simulations and prototyping to ensure reliability from the outset. Following the design phase, JAKA transitions to high-quality manufacturing, where prototypes are transformed into standardized products. The company employs automated production controls and stringent testing protocols to validate performance, accuracy, and safety. Each robotic arm undergoes thorough checks to guarantee that it meets high performance and safety standards before reaching customers. Once the cobots arrive at client sites, JAKA emphasizes the importance of integration and ongoing support. The company provides clear documentation, programming tools, and training resources to facilitate a smooth setup. Their design choices, such as a user-friendly programming interface, enhance the deployment process, ensuring that the cobots operate effectively within the customer's workflow. Through this comprehensive approach, JAKA Robotics aims to deliver not just automation hardware, but a reliable solution that continues to adapt and provide value throughout its service life. The company’s commitment to engineering integrity and practical usability positions it as a leader in the collaborative robotics industry.
Doosan Robotics Accelerates Push to Become an AI Robot Solutions Leader
Doosan Robotics is intensifying its efforts to become a leading global provider of intelligent robot solutions, with a comprehensive growth strategy that includes international acquisitions, talent recruitment, and organizational restructuring. On July 28, the company's board approved the acquisition of an 89.59% stake in ONExia, a Pennsylvania-based robotics system integrator, for approximately $25.9 million. Founded in 1984, ONExia specializes in end-to-end automation services across manufacturing, logistics, and packaging sectors, and has seen consistent annual sales growth of about 30%. This acquisition is seen as a pivotal move for Doosan Robotics, enabling it to enhance its competitiveness in the intelligent robotics market by shifting from a hardware-centric model to a more integrated approach focusing on AI and software solutions. ONExia's engineering expertise and extensive automation data are expected to significantly bolster Doosan's AI capabilities. In conjunction with the acquisition, Doosan Robotics is increasing its investments in research and development, actively recruiting specialists in robotics, AI, and software development, and reorganizing its R&D division to prioritize AI and humanoid technologies. A new R&D Innovation Center is also set to open by the end of the third quarter. CEO Kevin (Minpyo) Kim emphasized the importance of this acquisition in strengthening the company's global presence and internalizing AI technologies, aiming to position Doosan Robotics as a leader in the emerging Physical AI landscape.
Why Are Custom Harness Solutions Essential for Next Generation Technology?
In the realm of hardware development, a recurring issue has emerged where wiring is often considered an afterthought. Engineers invest significant time and resources into creating advanced electric powertrains and high-density sensor arrays, ensuring that the mechanical and software components are meticulously designed. However, a common oversight occurs when the physical connections fail to fit within the designated space, leading to potential setbacks in the project timeline. This problem is particularly pronounced when relying on off-the-shelf components that may not be compatible with innovative designs. As the industry continues to evolve, addressing these wiring challenges is crucial for the successful integration of new technologies.
Engineering Technology aerospace Cable assembly Cable management Custom harness solutions
New chip could help tiny robots traverse complex environments
A team of researchers has developed an innovative method for creating 3D maps to enhance navigation systems, utilizing a combination of an efficient algorithm and specialized hardware. This advancement, which was announced in October 2023, aims to significantly reduce the memory and power requirements typically associated with 3D mapping technologies. By streamlining the mapping process, the researchers hope to improve the efficiency and accessibility of navigation tools, making them more practical for a variety of applications, including autonomous vehicles and mobile devices. The integration of dedicated hardware with the algorithm allows for rapid map generation, which could lead to faster and more reliable navigation solutions in real-world scenarios.
Research Computer science and technology Algorithms Artificial intelligence Machine learning Robotics
Human-machine teaming dives underwater
A team of researchers is advancing the capabilities of collaboration between divers and autonomous underwater vehicles (AUVs) for maritime missions. This initiative, which began in late 2023, aims to enhance operational efficiency and safety in underwater exploration and tasks. The research is taking place at a leading marine technology institute, where experts are focused on creating innovative hardware and sophisticated algorithms that facilitate seamless communication and coordination between human divers and AUVs. The motivation behind this development stems from the increasing complexity of underwater missions, which require precise teamwork to navigate challenging environments and accomplish objectives effectively. By integrating advanced technology, the researchers hope to improve the overall effectiveness of maritime operations, making them safer and more efficient for both divers and AUVs alike.
Research Sensors Lincoln Laboratory Oceanography and ocean engineering Robotics Computer science and technology
Why Precision Swiss Machining is Critical to the Future of Robotics and Automation
The industrial production landscape is undergoing a significant transformation due to the rapid advancements in robotics, factory automation, and intelligent manufacturing systems. This shift is characterized by an increasing reliance on highly precise mechanical systems that can operate continuously with minimal deviation. As automation technologies evolve and become more interconnected, modern manufacturing environments are integrating collaborative robots, automated assembly systems, and sensor-driven equipment. These innovations are not only enhancing efficiency but also improving the accuracy and reliability of production processes. The ongoing evolution in this sector reflects a broader trend towards smarter, more automated manufacturing practices that aim to meet the growing demands of global markets.
Automation Engineering Factories automated manufacturing automation hardware automation newsRobotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
