Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
YY Group Launches Training Lab, Deploys Pilot Robotics in Singapore
YY Group Holding Limited, an AI-native workforce management and integrated facility management provider, has announced the launch of its Humanoid Robotics Training Lab as part of its ongoing AI training data strategy. This initiative, which was first introduced on April 22, 2026, aims to enhance the company's capabilities in developing advanced AI solutions. The lab will focus on training humanoid robots to improve efficiency in various operational tasks. The announcement marks a significant step for YY Group as it seeks to solidify its position in the rapidly evolving AI sector across Asia and beyond.
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Tech companies desperately want to film you doing chores
This week, Shift, an AI training startup, announced its initiative to offer free home cleaning services to residents of New York City. The company aims to gather video footage of the cleaning process to enhance its AI training data. Shift plans to expand this service to other cities, including London, as part of its growth strategy. While the offer may appeal to many, potential users should be aware that their participation involves providing the company with recorded footage of their homes during the cleaning.
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AI agents enhance autonomous inspections, revamping manual approval processes for drones and ground robots by DataRobot, Chevron, and NVIDIA.
DataRobot has announced a collaboration with Chevron U.S.A. Inc., a subsidiary of Chevron Corporation, to implement agent-based AI in edge environments. This partnership aims to enhance autonomous patrol and inspection operations at Chevron facilities. By leveraging advanced AI technology, the initiative seeks to improve operational efficiency and safety in the company's infrastructure.
Qing Tong Vision Launches MotionDecode Data Open Plan: 1000-Hour Motion Capture Dataset Now Open Source
Qing Tong Vision has launched the MotionDecode Data Open Plan, offering free access to a comprehensive 1,000-hour high-quality human motion dataset. This initiative, announced recently, is designed to enhance the development of humanoid robots and promote embodied intelligence by reducing research barriers and encouraging collaboration within the data ecosystem. The program is expected to support a wide range of applications, including robot training and motion generation, representing a pivotal advancement in the industrialization of embodied intelligence.
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Tactile Data Competition Begins: Qianjue's Gripper Transforms Robot Training
Qianjue Robotics has unveiled the XTac UMI G1, a groundbreaking wearable multi-modal data collection gripper aimed at addressing the challenges of embodied intelligence in robotics. The introduction of this innovative device comes in response to the industry's pressing need for high-quality tactile data, which is essential for training robots to perform complex tasks in real-world environments. By capturing detailed interaction data, the XTac UMI G1 seeks to bridge the existing gap between visual data and physical interaction, thereby enhancing the capabilities of robots. This development marks a significant step forward in improving robotic performance and adaptability in various applications.
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Chasing rumors: Car CEO's departure untrue; WeChat tests native AI assistant; Apple, Tesla supplier data leaked.
On June 23, the ChiNext Index experienced its largest decline of the year, falling over 4% during trading and closing down 3.84%, dipping below the critical 4200-point mark. This downturn followed a record high set just a day prior. The trading volume for the day reached approximately 901.65 billion yuan, a decrease of 118.9 billion yuan from the previous day. All ten of the index's top-weighted stocks saw declines, particularly those in the AI computing sector. In a separate development, Tata Electronics confirmed a significant data breach, with over 630GB of sensitive information leaked, including design and specification documents for key clients like Apple and Tesla. The company stated that it had initiated a response plan and that operations remained unaffected. Apple is reportedly conducting a thorough investigation into the incident. Meanwhile, SpaceX has entered a multi-billion dollar agreement with AI startup Reflection AI to provide computing resources, with payments set to begin in July and continue through 2029. In the robotics sector, Nvidia unveiled its "Halos for Robotics" safety system aimed at enhancing the security of physical AI applications, while Faraday Future introduced its industrial-grade robotic arm series at a robotics expo in Chicago. Additionally, Meta has paused an internal AI training program that tracked employee mouse movements due to data security concerns, and Oracle announced a workforce reduction of approximately 21,000 employees, marking a 13% decrease in its total workforce as part of a business restructuring.
Building a Highway for Embodied Intelligence: From Data Collection to Ecosystem Development, Leju's Training Ground 2.0
Leju has introduced an innovative training ground model designed to enhance embodied intelligence in robotics through improved data collection efficiency and consistency. This initiative, which emphasizes the importance of real-world data application, aims to create a robust ecosystem that significantly advances robotic capabilities. By focusing on gathering and utilizing data effectively, Leju seeks to drive forward the development of intelligent systems that can better interact with their environments. The model represents a strategic effort to harness data as a critical resource in the ongoing evolution of robotics, positioning Leju at the forefront of this technological advancement.
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Tongren Intelligence Secures Nearly 400 Million Yuan in Series A Funding, Launches 'Data-Free Training' Embodied Brain for Scalable Applications
Tongren Intelligence, a company incubated by the Chinese Academy of Sciences, has unveiled a pioneering 'embodied brain' technology that functions without the need for extensive data training. This innovative development has attracted nearly 400 million yuan in Series A funding, positioning the company to transform the smart manufacturing and defense sectors. By providing advanced robotic solutions, Tongren Intelligence aims to enhance operational efficiency and capabilities in these critical industries. The funding will support the further development and deployment of this cutting-edge technology, which promises to redefine traditional approaches to automation and robotics.
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AI may not need massive training data after all
Recent research has revealed that artificial intelligence (AI) can exhibit human-like behavior without the necessity for extensive training data. Scientists have redesigned AI systems to mimic the structure and function of biological brains, resulting in certain models demonstrating brain-like activity spontaneously, without prior training. This finding challenges the conventional data-intensive methods currently employed in AI development. The implications of this work suggest that smarter design strategies could significantly enhance learning efficiency while reducing both costs and energy consumption in AI systems.
Generalist AI Releases "Science of Pretraining" Deep Dive: Why Data Quality Trumps Volume in Robotics
Generalist AI has unveiled new insights into its pretraining methodology in a technical addendum related to its recent GEN-0 launch. The company introduced innovative metrics, including "Reverse KL," designed to evaluate the creativity of its models. Additionally, Generalist AI announced that its infrastructure can process an impressive volume of data, equating to 6.85 years of robotic experience each day. This advancement highlights the company's commitment to enhancing artificial intelligence capabilities and underscores its efforts to push the boundaries of machine learning technology.
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Google's Apptronik opens a 90,000 square foot "robot park": training humanoid robots with a data factory to walk towards...
Apptronik, a robotics company backed by Google, has inaugurated a 90,000 square foot facility known as a "robot park" dedicated to the training of humanoid robots. This state-of-the-art center, located in Austin, Texas, aims to enhance the capabilities of robots by utilizing a sophisticated data factory that allows them to learn and refine their walking abilities. The opening of the robot park comes as part of Apptronik's broader mission to advance humanoid robotics technology, driven by the increasing demand for automation and intelligent machines in various industries. By leveraging extensive data and innovative training methods, the facility is expected to significantly accelerate the development of robots that can perform complex tasks in real-world environments.
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Apptronik unveils Apollo 2 and a flagship data collection and training facility
Apptronik has introduced Apollo 2, a cutting-edge data collection and training platform designed to facilitate continuous learning through its deployment. This innovative system aims to enhance the capabilities of robotic technologies by providing a robust environment for data gathering and training processes. The announcement highlights Apptronik's commitment to advancing robotics and artificial intelligence, reflecting the growing demand for sophisticated training tools in these fields. The unveiling of Apollo 2 marks a significant step forward in the company's efforts to improve the efficiency and effectiveness of robotic systems.
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Tsinghua-Harvard Team's Acorn Robot Develops 'Zero-Data' Robot That Learns Through Instinct, Not Training Data
A team of researchers educated at Tsinghua University and Harvard has developed an innovative robot capable of learning physical manipulation without any prior training data. This groundbreaking technology relies solely on tactile sensors and an instinct-driven trial and error approach to tackle complex tasks, such as picking up a flat credit card. The project highlights a significant advancement in robotics, showcasing the potential for machines to adapt and learn in real-time, which could revolutionize various applications in automation and artificial intelligence.
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The World's Largest Embodied Haptic Dataset Launched: Daimon-Infinity - Open Source with 10x Training Efficiency!
In 2026, Daimon Robotics introduced the Daimon-Infinity dataset, which is recognized as the largest dataset of its kind, encompassing multimodal haptic data. This initiative, developed in collaboration with prominent research institutions, seeks to improve robotic tactile perception, a crucial aspect for advancing fine motor skills training in robotics. The dataset addresses a significant gap in haptic data availability, which is essential for enhancing the capabilities of robots in performing delicate tasks.
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Is "Dehydration Cooling" the Future for Data Centers in the AI Era? Insights from Microsoft's Waterless Cooling Strategy.
Microsoft has announced new water conservation measures for its data centers in response to the growing demand for AI and cloud services. The tech giant aims to achieve water positivity by 2030, targeting a 90% improvement in water usage efficiency compared to initial levels. To support this goal, Microsoft plans to implement waterless cooling systems in its latest facilities and utilize rainwater, thereby promoting sustainable infrastructure operations.
The Data Bottleneck: Why AGIBOT is Open-Sourcing its Real-World Training Library
AGIBOT has announced the launch of a multi-phase, industrial-grade dataset aimed at addressing the significant scaling challenges faced by the robotics industry. This initiative comes as robotics technology transitions from research environments to everyday use in homes. The dataset, which is expected to enhance the development and deployment of robotic systems, will be made available in phases, allowing for comprehensive testing and refinement. The move is part of AGIBOT's broader strategy to facilitate innovation and improve the efficiency of robotic applications, ultimately making them more accessible to consumers. This launch is particularly timely, given the increasing demand for advanced robotics solutions in various sectors.
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Collecting robot training data is dirty, unglamorous work. Some AI labs are already paying XDOF to do it.
Recent discussions in the field of artificial intelligence highlight a significant challenge facing the development of physical AI systems. Experts emphasize that in order for physical AI to achieve milestones comparable to those of large language models (LLMs), a critical data issue must be addressed. As of October 2023, the existing datasets are insufficient to support the complex learning and operational needs of physical AI. This gap in data could hinder progress and innovation in creating AI that can effectively interact with and navigate the physical world. Addressing this problem is essential for advancing the capabilities of physical AI, ensuring that it can perform tasks with the same proficiency as its software counterparts.
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MIT researchers channel AI to turn hand gestures into robot training data
Researchers have developed an innovative method to enhance the capabilities of humanoid robots, particularly in tasks such as grasping objects. This advancement involves the use of a specialized ultrasound wristband worn by a human instructor, which captures the intricate movements of muscles, tendons, and ligaments beneath the skin. By analyzing this data, the robots can learn to replicate these movements more effectively. The initiative, which began in late 2023, aims to improve the dexterity and functionality of robots in various applications, from manufacturing to personal assistance. The ultrasound technology provides real-time feedback, allowing the robots to adjust their movements based on the instructor's actions. This approach not only enhances the robots' ability to perform complex tasks but also opens new avenues for human-robot interaction. The research is being conducted at a leading robotics lab, where experts are focused on bridging the gap between human-like movement and robotic precision. By mimicking the natural motion of human hands, the robots are expected to achieve greater efficiency and adaptability in their operations. This breakthrough could significantly impact industries that rely on automation, making robots more versatile and capable of handling delicate tasks that require a human touch.
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AIRoA collects 80,000 hours of robot operation data through industry-academia collaboration for physical AI infrastructure.
The AI Robot Association (AIRoA) released a YouTube video on May 29, 2026, showcasing a groundbreaking initiative titled "A Massive Collaborative Physical AI Data Initiative." The video highlights the ongoing operations of robots across various universities and research institutions, illustrating the accumulation of a global data infrastructure. This initiative aims to enhance the development of artificial intelligence by creating a comprehensive database that supports collaborative research and innovation in robotics. Through this project, AIRoA seeks to foster advancements in AI technology and improve its applications in real-world scenarios.
ugo and FastLabel launch training program for developing VLA model with domestic humanoids and physical AI.
Ugo Corporation and FastLabel Inc. have launched a hands-on training program aimed at facilitating the development of Vision-Language-Action (VLA) models for companies, universities, and research institutions. This initiative utilizes the domestically produced humanoid robot, the "ugo Pro R&D model," to support participants from the initial stages of model development. The program, titled "ugo VLA Model Development Training Program powered by FastLabel," is designed to enhance practical skills and knowledge in the emerging field of VLA technology.
Physical AI’s looming data rights battle: Interview with Kate Shen of Anaxi Labs
As artificial intelligence technology advances and integrates more into everyday life, industry experts are shifting their focus from the capabilities of robots and AI models to the ownership of the data that powers these innovations. This growing concern has emerged as a critical topic among stakeholders, prompting discussions about data rights and the implications for both developers and users. The conversation is gaining momentum as the demand for vast datasets to train AI systems increases, raising questions about privacy, consent, and the ethical use of information. As the AI landscape evolves, understanding data ownership will be essential for shaping future regulations and ensuring fair practices in the burgeoning field of physical AI.
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The Shift in Physical AI: Qunke Technology Develops a Simulation Data Production Line
Qunke Technology has introduced a pioneering solution to tackle the pressing shortage of high-quality 3D training data, which is vital for the advancement of the physical AI industry. As leading companies in embodied intelligence shift their focus from model architecture to data infrastructure, Qunke's innovative simulation data production line aims to fill this gap. The company’s efforts have been recognized at the European Conference on Computer Vision (ECCV), where three of its groundbreaking research papers were accepted. These contributions are expected to set new benchmarks in the fields of spatial intelligence and data synthesis, further propelling the development of AI technologies.
Embodied Intelligence Simulation Data 3D Training Data AI Benchmarking
Bee Technology Aims to Solve Robotics Data Challenges Starting with a Cup
Bee Technology is making strides in the robotics sector by tackling the challenges of teaching robots to execute physical tasks, such as picking up objects. The company has recently obtained substantial funding to advance its MEgo series, which encompasses both hardware and data processing technologies. This initiative aims to establish a robust data supply chain essential for developing embodied intelligence in robots. By prioritizing high-quality physical AI data, Bee Technology is positioning itself as a key player in the industry, targeting businesses that depend on reliable data for training and optimizing their robotic models.
Embodied Intelligence Robotics Data Infrastructure AI Data Collection MEgo Hardware Data Processing Technology
How Musicians Can Get Paid for Training AI
In response to the challenges posed by generative AI on the music industry, startups like Sureel and SoundVerse are developing innovative solutions to ensure musicians are compensated fairly for their work. Following Warner Music Group's acquisition of Sureel, the company has partnered with the Swedish copyright agency STIM to create a system that tracks how music is used in AI training. This software allows creators to specify the terms of use for their music, ensuring they receive royalties based on its influence in AI-generated outputs. The ongoing debate centers on how to accurately attribute the contributions of various training data to the outputs produced by AI systems. SoundVerse advocates for a model that rewards artists continuously throughout the AI lifecycle, rather than through one-time payments. This approach aims to maintain the economic incentives that drive creativity while addressing concerns about AI's potential to undermine cultural vibrancy and artist livelihoods. As copyright lawsuits give way to negotiated agreements between major music labels and AI companies, there is a growing opportunity to establish fair compensation practices. Experts emphasize the need for transparent and equitable attribution systems that reflect the complex relationship between training data and AI outputs. Ultimately, the success of these initiatives may depend on collaboration across disciplines, including musicology, law, and economics, to create policies that support a sustainable creative sector in the age of AI.
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India's $1/hour Data Collection Model Gains Popularity
A novel data collection model is emerging in India, utilizing head-mounted cameras worn by workers to capture first-person footage. This innovative approach, spearheaded by the teenage founders of Egolab AI, has garnered significant attention and was recently acquired by a US company, highlighting its growing importance in the industry. Additionally, the startup Human Archive has successfully raised $8.2 million to enhance this data collection method. This initiative not only aims to provide valuable data for artificial intelligence training but also offers workers an opportunity to earn supplementary income. The combination of technology and economic support is positioning these startups at the forefront of a transformative movement in data collection.
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Scientists show predictable training can outperform complex robot learning data
Researchers are making significant strides in developing robots capable of manipulating objects with human-like dexterity, a challenge that has long posed difficulties in the field of robotics. This advancement is crucial as it could enhance the ability of robots to perform complex tasks in various settings, including homes, hospitals, and manufacturing plants. The ongoing work, which has gained momentum in recent months, is taking place in laboratories across the globe, where teams are experimenting with advanced algorithms and machine learning techniques. The motivation behind this research stems from the increasing demand for robots that can assist in everyday tasks, improve efficiency in industrial processes, and provide support in healthcare environments. By mimicking the intricate movements of the human hand, researchers aim to create robots that can handle delicate objects and perform tasks that require precision and adaptability. To achieve this, scientists are employing a combination of innovative hardware designs and sophisticated software programming. They are utilizing sensors and artificial intelligence to enable robots to learn from their interactions with various objects, refining their skills over time. This iterative learning process is essential for developing robots that can operate effectively in unpredictable environments. As the field progresses, the implications of these advancements could revolutionize how robots are integrated into daily life, making them more versatile and capable of performing a wider range of functions. The ongoing research highlights the potential for robots to not only assist but also enhance human capabilities in numerous domains.
Decentralized Training Can Help Solve AI’s Energy Woes
As the demand for artificial intelligence (AI) continues to surge, concerns over its significant energy consumption and carbon footprint have prompted major tech companies to explore nuclear energy as a sustainable solution. While nuclear-powered data centers remain a future prospect, industry leaders are currently focusing on decentralizing AI model training to address the escalating energy requirements. This approach distributes training tasks across a network of independent nodes, utilizing existing computing resources, such as dormant servers and solar-powered home computers, rather than relying solely on traditional data centers. Companies like Nvidia and Cisco are enhancing their infrastructure to support this decentralized model, allowing for efficient AI training across geographically dispersed data centers. Additionally, platforms like Akash Network are facilitating a "GPU-as-a-Service" model, enabling users with underutilized GPUs to rent out their computing power. On the software side, advancements in federated learning and algorithms like DiLoCo are being implemented to optimize decentralized training while minimizing communication costs and enhancing fault tolerance. These innovations allow for collaborative model training without the need for constant data exchange, thus improving efficiency. Akash Network's Starcluster program aims to convert homes into functional data centers by leveraging solar energy and existing computing devices. This initiative seeks to make participation accessible and is targeting a 2027 launch. By decentralizing AI training, the industry hopes to create a more energy-efficient and environmentally sustainable future for AI development.
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Decart’s Oasis 3 world model streams realism into robotic training environments
Decart, a leading frontier AI research lab, has unveiled its latest world model, Oasis 3, in a bid to integrate synthetic simulation with physical AI. The announcement, made recently, highlights the model's capability to enhance the training processes for operating system models used in robots and autonomous vehicles. By focusing on this innovative approach, Decart aims to advance the development of intelligent systems that can operate seamlessly in real-world environments. The launch of Oasis 3 represents a significant step forward in the quest to improve AI's practical applications, addressing the growing demand for more sophisticated and capable autonomous technologies.
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How defense teams can scale AI without increasing data risk
Artificial Intelligence (AI) is significantly enhancing the mission data footprint within defense environments, prompting a growing demand for scalable and layered protection of data-at-rest (DAR). This development is crucial as defense sectors increasingly rely on vast amounts of sensitive information to support their operations. The expansion of AI capabilities is occurring in response to the evolving complexities of modern warfare and cybersecurity threats, necessitating robust data protection strategies. As military and defense organizations seek to secure their information assets, the implementation of advanced DAR solutions will play a vital role in safeguarding critical data against potential breaches and ensuring operational integrity.
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Robo.ai Acquires Data Processing and Compression Tech Company Neurovia for $100M
Robo.ai Inc. (NASDAQ: AIIO) has entered into an agreement to acquire Neurovia AI Limited, a company focused on data processing and compression, for $100 million. This acquisition aims to enhance Robo.ai's technological capabilities and expand its market presence. The transaction is expected to close pending the fulfillment of certain conditions.
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AirData and BRINC Integration Brings Automated Flight Records to Public Safety Drone Programs
AirData has launched a new integration with BRINC to enhance the management of drone flight data for public safety agencies. This collaboration enables automatic capture and organization of flight records from BRINC’s Lemur 2 and Responder drones within the AirData platform. By streamlining this process, the integration aims to reduce the reliance on manual data entry, thereby supporting scalable and auditable drone operations. This development is expected to significantly improve the efficiency of drone programs used in public safety, allowing agencies to focus more on their core missions.
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DWTEK Begins 2026 With ROV Training Program in Malaysia
At the start of 2026, DWTEK has launched its first major initiative of the year, an ROV training session, in collaboration with clients SWEN Holdings and DEEPSEE. This event is taking place in Malaysia and underscores the increasing demand for subsea capabilities in the region. The hands-on training program aims to equip participants with essential skills and knowledge, reflecting the industry's commitment to enhancing operational proficiency in underwater technology.
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New Infrastructure for Humanoid Robot Training Set to Surge by 2026
On June 8, the Ministry of Industry and Information Technology, alongside the State-owned Assets Supervision and Administration Commission, unveiled a collaborative initiative aimed at advancing humanoid robot training by 2026. This initiative seeks to establish practical training environments and validate applications in critical scenarios, signaling a transformative shift towards a data-driven infrastructure. The move is expected to redefine the humanoid robotics industry and enhance its competitive dynamics, reflecting a commitment to innovation and technological advancement in this rapidly evolving field.
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36Kr Exclusive: Four Key Propositions for ByteDance's AI by 2026
ByteDance is setting ambitious goals for its AI initiatives in 2026, focusing on four key areas. The company aims to enhance world model training, targeting performance levels comparable to Google's leading model, Genie 3, by the end of the year. Additionally, ByteDance plans to maintain its leadership in video models while exploring new avenues like dynamic generation. The company is also committed to strengthening its coding capabilities, emphasizing the importance of data feedback and evaluation to improve agent performance, particularly in office applications. Despite recent advancements, including the launch of Seed 2.0 and Seedance 2.0, ByteDance faces challenges in the world model arena, having entered the field later than competitors. The company established a research group in 2025 to explore visual-language-action models and has since set a clear goal for world model development. However, internal assessments indicate that performance still lags behind global standards by approximately 10%. In parallel, ByteDance is accelerating the commercialization of its Doubao platform, which has seen a surge in daily active users, reaching 200 million. The company plans to introduce paid features and enhance its offerings for professional users, particularly in sectors like finance and law. Doubao's strategy includes differentiating itself in the crowded AI tools market and expanding its presence internationally, with a focus on small language markets. As ByteDance navigates these challenges, it aims to leverage its engineering expertise and data resources to emerge as a leader in the evolving AI landscape.
Cubic wins Italian Army SIM 2.0 live training modernisation contract
Cubic Defense has been awarded a contract by the Italian Army to enhance its live training capabilities and implement data-driven methodologies throughout its operations. This initiative aims to modernize the army's training processes, ensuring that personnel are equipped with advanced skills and knowledge. The contract reflects a growing trend among military forces to leverage technology and data analytics to improve training effectiveness and operational readiness. The collaboration is expected to take place over the coming months, with Cubic Defense providing innovative solutions tailored to the specific needs of the Italian Army.
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NVIDIA Announces Open Physical AI Data Factory Blueprint to Accelerate Robotics, Vision AI Agents and Autonomous Vehicle Development
NVIDIA has unveiled the NVIDIA Physical AI Data Factory Blueprint, an innovative open reference architecture designed to streamline the generation, augmentation, and evaluation of training data for physical AI applications. Announced today, this blueprint aims to significantly cut costs, time, and complexity associated with training AI models. By providing a unified and automated approach, NVIDIA seeks to enhance the efficiency of AI development processes, making it easier for organizations to implement and scale their AI initiatives. This initiative reflects NVIDIA's commitment to advancing AI technology and supporting developers in overcoming the challenges of data management in AI training.
Project Go-Big: Internet-Scale Humanoid Pretraining and Direct Human-to-Robot Transfer
Figure has unveiled Project Go-Big, an innovative initiative aimed at developing the largest humanoid pretraining dataset in collaboration with Brookfield. This ambitious project is designed to enhance the capabilities of robots, allowing them to learn navigation and manipulation tasks directly from human-generated video content. By achieving zero-shot transfer of skills, Project Go-Big is set to significantly advance the field of humanoid robotics. The announcement comes as the demand for more sophisticated robotic systems continues to grow, highlighting the importance of effective training methods in the evolution of robotics technology.
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Figure Taps Brookfield's Global Real Estate Portfolio to Scale AI Training
Figure, a humanoid robotics company, has formed a strategic partnership with asset management firm Brookfield. This collaboration will provide Figure with access to an extensive portfolio of real estate, enabling the company to develop a significant real-world training dataset for its Helix AI model. The partnership aims to expedite the commercial deployment of Figure's robotics technology. By leveraging Brookfield's diverse properties, Figure seeks to enhance the capabilities of its AI, ultimately advancing the integration of humanoid robots into various sectors.
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New Paradigm for Stroke Gait Training: Therapist-Exoskeleton-Patient Interaction
A research team from Northwestern University and Shirley Ryan AbilityLab has unveiled an innovative approach to stroke rehabilitation that integrates a therapist-exoskeleton-patient interaction model. This groundbreaking method enables both therapists and patients to don exoskeletons, facilitating real-time interaction during gait training sessions. The new technique aims to improve the effectiveness of rehabilitation and boost patient engagement, offering a significant advancement over conventional manual training methods. The introduction of this technology marks a promising development in the field of stroke recovery, potentially transforming how therapists assist patients in regaining mobility.
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Doosan, LG CNS link up for data centers, robotics and AI
Doosan Corp. has announced a strategic partnership with LG CNS aimed at enhancing global competitiveness in key technology sectors, including data centers, hydrogen drone logistics, and artificial intelligence. The memorandum of understanding was signed by Doosan Corp. President Yoo Seung-woo and LG CNS President Hyun Shin-gyoon during a ceremony at LG Science Park in western Seoul on Thursday. This collaboration will focus on developing data center and cloud services, positioning both companies to leverage their strengths in these rapidly evolving industries. The partnership reflects a commitment to innovation and technological advancement in response to the growing demand for efficient and sustainable solutions in the digital landscape.
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RTK Data Coverage Explained: Why Access to Many RTK Stations Matters
Accurate field positioning relies not only on high-quality GNSS receivers but also on the effectiveness of the correction networks that support them. Factors such as the distance from correction stations, signal quality, system uptime, and regional availability play crucial roles in determining the stability of the final positioning results. Field teams can achieve enhanced RTK precision by utilizing RTKdata as a provider, which offers extensive access to a robust network of correction services. This integration ensures that teams can maintain strong positioning accuracy in various operational environments.
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SpaceX sets IPO price to raise $75 billion; OpenAI CEO delays South Korea visit; new AI complaint center launched.
OpenAI CEO Sam Altman has postponed his planned visit to South Korea, originally scheduled for June 14-15, due to personal reasons. During the visit, he was expected to meet with leaders from major companies including Samsung Electronics, Kakao, and NAVER. In a separate announcement, Waymo, the autonomous driving subsidiary of Alphabet, revealed a new $30 monthly membership plan called Waymo Premier, aimed at invited users. This plan will offer benefits such as priority rides, a 10% cashback on trips, and the ability to cancel rides up to five times a month at no cost. Initial invitations will be sent to eligible passengers in San Francisco, Los Angeles, and Phoenix, with plans to expand to other cities. Meanwhile, SK Hynix is exploring the integration of AI technologies, including ChatGPT, into its operations. CEO Lee Seok-hee indicated that the company is balancing the protection of industrial technology with the adoption of external AI services, considering tools like Microsoft 365 and CoPilot. In financial news, major Wall Street banks have begun restricting hedge funds' leverage on Asian chip stocks, including SK Hynix and Samsung, due to concerns over potential market corrections. This move involves raising financing costs for hedge fund bets and limiting new transactions. Additionally, Google announced a $50 million investment to train U.S. tech workers, addressing the growing demand for AI infrastructure. This investment is part of a broader initiative that has already seen over $1 billion allocated to training programs since 2022. Lastly, SK Hynix reported that a fire at its Cheongju plant on June 12 has been brought under control, with production equipment operating normally.
Blackstone-Backed AirTrunk Pledges $30B for Indian AI Data Center Expansion
AirTrunk, an Australian data center operator supported by Blackstone, has unveiled a significant $30 billion investment plan aimed at developing 5 gigawatts of data center capacity in India by 2030. This initiative marks one of the largest infrastructure commitments in the country’s burgeoning artificial intelligence sector. The announcement followed a meeting between AirTrunk's Chief Executive Robin Khuda and Indian Prime Minister Narendra Modi, underscoring the strategic importance of this investment in bolstering India's technological infrastructure and supporting its growing demand for data processing capabilities.
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AirData UAV and LeoSight Bring Live Drone Data for DFR Programs
LeoSight has announced a new integration with AirData UAV that enables real-time streaming of flight data, telemetry, and operational insights directly into its command software, LeoCommand. This integration aims to enhance collaboration between dispatchers and field teams by providing shared visibility across drone and DFR (Drone First Response) operations. The partnership leverages AirData's status as one of the most widely used platforms in commercial drone operations, allowing public safety agencies to improve their operational efficiency. The integration is expected to streamline communication and data sharing, ultimately enhancing response capabilities in critical situations.
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LoongForge Achieves 2.3x Training Throughput Improvement for GR00T N1.6 Model
LoongForge, a company led by Baido Baige, has announced significant improvements in the training process of its GR00T N1.6 VLA model. This optimization, which was implemented recently, has resulted in a remarkable 56.6% reduction in training cycles and a 2.3-fold increase in throughput. The enhancements specifically target challenges such as input/output blocking and inefficient scheduling, thereby greatly boosting the overall efficiency of the model's training process. These advancements are expected to streamline operations and improve performance in various applications.
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How Melbourne’s AI and Data Center Flywheel Is Accelerating Research Innovation
Melbourne is solidifying its status as a global hub for artificial intelligence (AI) research and advanced engineering, driven by significant investments in infrastructure and a growing concentration of talent. The city, renowned for hosting major events like the Australian Open and Formula 1 Grand Prix, is now leveraging its organizational capabilities to support large-scale compute and data-intensive research. In February 2026, Monash University unveiled MAVERIC, Australia's largest university-based AI supercomputer, developed in collaboration with NVIDIA and Dell Technologies. This state-of-the-art facility is designed to enhance medical research, enabling Australian scientists to work with sensitive datasets securely. The supercomputer exemplifies Melbourne's commitment to fostering cross-disciplinary collaborations and advancing research in fields such as cancer detection and drug discovery. Melbourne's infrastructure is further bolstered by the expansion of data centers, including CDC Data Centres' new campus, which will provide over 800 megawatts of digital capacity essential for AI workloads. The city's strategic investments, including a AUD $2 billion AI infrastructure hub in Fishermans Bend, are positioning it as a national leader in high-performance AI. Moreover, Melbourne's selection to host international technology conferences, such as Data Center World Australia and The AI Summit Australia in September 2026, underscores its growing influence in the global AI landscape. These events facilitate knowledge transfer and collaboration among researchers, reinforcing Melbourne's role as a key player in the future of AI and data-driven research.
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Microsoft’s AI data center push is colliding with its clean power goals
Microsoft's ambitious plans to expand its data center operations are jeopardizing its commitment to clean energy initiatives. The tech giant is facing scrutiny as it seeks to increase its data center footprint, which could conflict with its established goals for sustainable power usage. As the company accelerates its infrastructure development, concerns have arisen regarding the environmental impact and the ability to source renewable energy to meet its growing energy demands. This situation is unfolding amid a broader industry trend where the demand for cloud services continues to surge, prompting companies like Microsoft to invest heavily in new facilities. The challenge lies in balancing rapid growth with the need to adhere to environmental standards and commitments, raising questions about the future of corporate responsibility in the tech sector.
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Tech Week Shanghai founding edition to connect global tech players with China’s data ecosystem
Tech Week Shanghai will take place on May 6 and 7 in Shanghai, marking its inaugural event. This conference and exhibition aims to unite leaders from the enterprise technology sector, including exhibitors, policymakers, and industry practitioners. Participants will engage in discussions and showcase innovations across various domains such as cloud computing, cybersecurity, data center infrastructure, data governance, data services, artificial intelligence, and enterprise digital transformation. The event seeks to foster collaboration and knowledge sharing among professionals to drive advancements in these critical areas of technology.
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Volocopter Unveils VoloXPro and Expands into Pilot Training Market, Air Sports
Volocopter has launched the VoloXPro, an innovative electrically powered ultralight multicopter aimed at private pilots, training organizations, and future air taxi operators. Announced recently, this new aircraft is designed to cater to both private and commercial sectors, with a particular focus on serving flight schools, flying clubs, and air sports enthusiasts throughout Europe. The introduction of the VoloXPro marks Volocopter's strategic expansion into the pilot training market and air sports, reflecting the company's commitment to enhancing aviation accessibility and education. This development is expected to bolster the growing demand for advanced training solutions and recreational flying options as the aviation industry evolves.
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The AI Data Centers That Fit on a Truck
In response to the growing demand for rapid deployment of AI hardware, companies like Duos Edge AI and LG CNS are shifting towards modular data centers. Traditional data centers, which require extensive construction of steel and concrete shells, can take years to build, posing challenges for organizations eager to implement AI solutions. Duos Edge AI, led by CEO Doug Recker, has developed modular compute pods that can be deployed in about six months, significantly faster than conventional setups. Each 55-foot pod houses racks of GPUs and can operate independently or in conjunction with others, with a recent deal to deploy four pods containing a total of 2,304 GPUs, expandable to 4,608. Similarly, LG CNS is launching its AI Modular Data Center in Busan, South Korea, featuring 576 Nvidia GPUs per unit, with plans for an expanded version supporting over 4,600 GPUs. Both companies emphasize the advantages of modular systems, which require only a concrete pad for installation, allowing for quicker site readiness and reduced permitting complexities. The modular approach not only accelerates deployment but also offers cost savings, with Duos estimating a 5-megawatt modular deployment could be built for around $25 million, significantly lower than traditional facilities. As the market for modular data centers is projected to double by 2030, other tech giants like Hewlett Packard Enterprise and Schneider Electric are also exploring similar solutions. The modular design allows for incremental expansion, enabling facilities to grow in capacity as demand increases, positioning them as a viable alternative to traditional data centers.
Data-center Networking Liquid-cooling AiRobotics needs a service framework.
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