Mobisense Systems is an AI-powered private 5G solutions provider for industrial environments including seaports, factories, and logistics hubs. It transforms 5G radio signals into actionable intelligence, supporting Industry 4.0 connectivity, real-time positioning, and mission-critical automation applications.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
On July 15, Ofo, the bike-sharing company, unexpectedly updated its official WeChat account after nearly five years of silence. The post was a recruitment notice for patients with skin diseases, unrelated to its bike-sharing business, yet it sparked significant public interest regarding unrefunded deposits. Many users expressed their concerns, asking when their deposits would be returned. Despite widespread belief that Ofo had ceased operations, records indicate that its associated operational entity, Beijing Baike Lock Technology Co., Ltd., remains active. However, the company faces high-risk information, including numerous legal cases and enforcement actions, which raises questions about its future viability. As Ofo navigates this uncertain landscape, stakeholders should monitor any developments regarding its operational status and the potential for deposit refunds. No further timeline was disclosed at the time of publication.
mimic Robotics has unveiled a new robotic hand, the mimic hand M1, along with the mimic wearable U1 exoskeleton and a proprietary software platform. This integrated system aims to enhance general-purpose dexterous manipulation in industrial robots by addressing the challenge of collecting high-quality training data for AI models that perform human-like tasks. The significance of this launch lies in mimic Robotics' approach to design, which focuses on human hand morphology rather than traditional two-finger grippers. The mimic hand M1 features 15 actuated degrees of freedom and is capable of handling payloads over 25 kg, while the mimic wearable U1 allows human operators to demonstrate tasks in real-time, improving data collection for AI training. Looking ahead, the company’s innovative middleware and teleoperation software are expected to enhance robot control and AI inference speed. No further timeline was disclosed at the time of publication.
Components Computing News ai automation dexterous manipulationWarehouse automation is evolving from a mere operational efficiency tool to a strategic priority for organizations, driven by ongoing labor shortages and rising fulfillment expectations. Inteq, a UK-based warehouse automation specialist, emphasizes the importance of integrating robotics and software to create flexible, hardware-agnostic solutions that adapt to changing operational needs. Jon Roberts, sales director at Inteq, highlights that software, particularly Warehouse Execution Systems, is now the critical component of successful automation projects. These systems serve as the central hub connecting robotics, equipment, and personnel, enabling more efficient operations. The shift towards software-first strategies reflects a broader trend where businesses recognize the risks of inaction in the face of increasing competition and complexity in supply chains. Looking ahead, companies investing in integrated automation platforms are likely to gain a competitive edge in logistics and fulfillment. The conversation also touches on the growing role of AI in predictive operations and the transition of autonomous mobile robots into essential warehouse infrastructure, supported by innovative commercial models like Robotics-as-a-Service. No further timeline was disclosed at the time of publication.
Features Logistics Supply chain Warehouse robots amrs artificial intelligencePanasonic Connect is at the forefront of transforming manufacturing systems as electronics become smaller and more complex. The company offers smart manufacturing solutions that integrate surface-mount technology (SMT), robotics, AI analytics, and connected factory platforms. Through its Gemba Process Innovation strategy, Panasonic Connect aims to enhance productivity and operational resilience in factories. The increasing complexity of electronics, particularly in vehicles and industrial systems, is reshaping production line design and operations. Scott Zerkle, associate director of technical operations at Panasonic Connect North America, emphasizes that AI's primary value lies in supporting factory workers through predictive maintenance and defect detection, rather than replacing them. He highlights the need for manufacturers to connect data across various production elements to foster continuous improvement. Looking ahead, Zerkle predicts that the next significant advancement in smart manufacturing will involve integrating AI tools with data from machines and materials. This integration will enable factories to learn from their production history, ultimately enhancing efficiency and adaptability in high-mix production environments. No further timeline was disclosed at the time of publication.
Communications Computing Digital Automation Features Manufacturing aiAetina, a specialist in edge AI computing, is set to enhance its offerings by adding support for the newly unveiled Nvidia Jetson T3000 and T2000 modules. These modules are designed for robotics and industrial edge AI applications, and Aetina plans to integrate them into its DeviceEdge AIE-KT and AIE-PT systems, providing scalable and efficient AI computing solutions. This expansion is significant as it allows Aetina to leverage Nvidia's advanced AI capabilities, including the ability to run generative AI models with low latency, which is crucial for applications like humanoid robots. The Nvidia Jetson T3000 module boasts up to 865 FP4 TFLOPS, while the T2000 offers up to 400 FP4 TFLOPS, catering to a range of robotics applications from visual AI agents to autonomous mobile robots. Looking ahead, Aetina is among the first partners to support these new modules, with Nvidia expecting them to be available in the first quarter of 2027. The company plans to provide further details on system specifications and developer support as the launch date approaches, making it a key player in the evolving landscape of AI-driven robotics solutions.
Computing Industry Aetina automation Autonomous robots DeviceEdge AIE-KTStrider Robotics, an Indian startup, has successfully demonstrated a quadruped robot capable of carrying a 40 kg payload across challenging terrains. This marks a significant step as the Bengaluru-based company transitions from prototype development to commercial deployment, with field pilots currently underway with a major oil and gas company and an automotive manufacturer. The importance of this development lies in Strider's focus on enhancing India's domestic robotics manufacturing capabilities, as over 80 percent of the robot's components are sourced locally. Quadruped robots are gaining traction in industries where traditional wheeled vehicles face limitations, such as energy, mining, and infrastructure inspection, due to their ability to navigate uneven surfaces and carry equipment. Looking ahead, Strider Robotics aims to solidify India's position as a key player in the development of intelligent legged robotic systems for industrial and defense applications. No further timeline was disclosed at the time of publication.
Robotics Robotics and Automation Robotics and Automation News Robotics News AI software artificial intelligenceAt the WAIC, RoboScience showcased a groundbreaking cloud-based embodied large model named Visics, capable of controlling multiple robotic hands. This innovation allows for seamless switching between different robotic hands while maintaining operational efficiency, demonstrating the ability to recognize and grasp various objects autonomously within 30 seconds. The significance of this development lies in its potential to revolutionize robotic operations across diverse applications. By enabling a single model to adapt to various hand configurations, Visics enhances the versatility of robotic systems, allowing them to perform complex tasks without the need for extensive retraining when hardware changes occur. Looking ahead, the industry will be keen to observe how Visics performs in real-world scenarios and its ability to execute long-term tasks by integrating multiple actions. No further timeline was disclosed at the time of publication.
Robotics Cloud Computing AI Automation Object RecognitionIn 2026, the embodied intelligence industry is transitioning from technology validation to large-scale commercialization. With support from capital and policy, the focus is shifting from rigid, high ROI scenarios like industrial manufacturing to commercial services and home environments. A key challenge remains: can robots effectively operate in the complex and unstructured home settings? The complexity of home environments poses significant challenges for humanoid robots, which must navigate unclear instructions and varied layouts. Current mainstream navigation AI struggles with personalized understanding, a critical shortcoming for practical applications in home services. At the World Artificial Intelligence Conference 2026, Fourier showcased the 'Embodied Home' solution, aiming to enable robots to understand their environment and intentions, thereby completing long sequences of tasks autonomously. The 'Embodied Home' solution integrates a semantic task execution hub for humanoid robots, combining large language models, 3D spatial semantic memory, navigation planning, and control execution. This allows robots to autonomously interpret and execute tasks based on natural language commands, enhancing user interaction and ensuring task reliability through a complex task scheduling mechanism. No further timeline was disclosed at the time of publication.
Robotics AI Home Automation Semantic UnderstandingOn July 17, 2026, the World Artificial Intelligence Conference (WAIC) commenced in Shanghai, focusing on the theme 'Intelligent Partners, Co-Creating the Future.' Dahuang Technology presented its AI multimodal capabilities, showcasing a comprehensive system that includes understanding, connecting, interacting, and controlling complex data. The demonstration highlighted the company's transition from video-centric solutions to a broader AI multimodal framework. Dahuang Technology's Sports AI system, based on the BlackEye multimodal spatial model, was a key feature, providing real-time analysis of sports events. This system can automatically label players, track movements, and generate slow-motion replays, demonstrating the AI's ability to comprehend complex video scenarios. The technology has already been validated in high-profile events like the 2026 World Cup and the Paris Olympics. The company also introduced its AI multimodal perception compression technology, achieving over tenfold compression efficiency while maintaining video quality. This capability is crucial for applications in drone inspections, remote communications, and robotic operations, addressing the growing demands for bandwidth and data transmission efficiency in various sectors. No further timeline was disclosed at the time of publication.
AI Multimodal Technology Video AI Data Compression Sports AI Human-AI InteractionAt the 2026 World Artificial Intelligence Conference (WAIC), BrainCo showcased a brain-controlled robot training platform. This innovative platform allows users to control robotic arms through brainwave signals, enabling actions like pouring water and picking apples without traditional interfaces. The significance of this technology lies in its ability to bridge the gap between human intention and robotic action. By decoding brain signals, the platform facilitates a direct communication pathway, allowing robots to understand and execute tasks based on users' thoughts. This advancement simplifies the integration of brain-machine interfaces with robotics, making it accessible to researchers without specialized backgrounds. Looking ahead, BrainCo's platform supports various robotic types, including humanoid robots and robotic arms, and is adaptable for ongoing developments. Researchers can select robots based on their study focus, whether for grasping tasks or complex human-robot interactions. No further timeline was disclosed at the time of publication.
Brain-Controlled Robots AI Research Neuro-Embodied AI Human-Robot Interaction
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