Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
SynapX Launches SYNData: Multimodal Data Collection System for Embodied AI Era
SynapX has unveiled SYNData, an innovative multimodal data collection system designed to enhance dexterous manipulation capabilities in robotics. This cutting-edge system integrates ego vision, electromyography (EMG) signals, and data from exoskeleton gloves, facilitating the scalable collection of human manipulation data essential for advancing robot learning. The launch of SYNData aims to bridge the gap between human dexterity and robotic functionality, providing researchers and developers with comprehensive tools to improve robotic performance. This development is particularly significant as it addresses the growing demand for more sophisticated and adaptable robotic systems in various applications.
Robotics
SynapX Launches SYNData: Multimodal Data Collection System for Embodied AI Era
SynapX has introduced SYNData, an innovative multimodal data collection system designed to enhance dexterous manipulation capabilities. Launched recently, this system integrates ego vision, electromyography (EMG) signals, and data from exoskeleton gloves, facilitating the scalable collection of human manipulation data essential for advancing robot learning. The development aims to improve the interaction between humans and robots, ultimately contributing to more sophisticated robotic applications in various fields. By harnessing diverse data sources, SYNData promises to provide valuable insights that can drive the evolution of robotic dexterity and functionality.
Robotics
Tsinghua University and Shouyi Technology Launch EgoEMG Dataset for Hand Pose Estimation
A groundbreaking dataset, known as EgoEMG, has been launched through a collaboration between Tsinghua University and Shouyi Technology. This dataset is notable for being the first public resource to offer synchronized multimodal data specifically designed for hand pose estimation, incorporating both electromyography (EMG) and visual signals. Released in October 2023, EgoEMG aims to address existing challenges in hand perception for robotics. By providing comprehensive data that reflects human hand movements, the dataset seeks to enhance the capability of machines to learn and perform dexterous tasks through human demonstration. This initiative represents a significant step forward in the field of robotics, potentially improving the interaction between humans and machines in various applications.
Hand Pose Estimation Multimodal Data Robotics EMG Technology
Noetra Initiates Development of Japan's Multimodal AI Foundation Model for Robotics
Noetra, in collaboration with key partners including Sony, SoftBank, NEC, and Honda Motor, has launched extensive R&D for a multimodal foundation model aimed at enhancing AI-enabled robotics in Japan. This initiative is part of a broader effort to develop sovereign AI technologies within the country, supported by investments from 44 companies across various sectors, primarily manufacturing. The significance of this development lies in its potential to position Japan as a leader in physical AI. By creating a robust multimodal foundation model, Noetra aims to improve industrial competitiveness and address societal challenges through advanced AI capabilities, including natural language processing and multimodal data understanding. Looking ahead, Noetra plans to construct AI computing infrastructure with Nvidia's advanced GPUs, with operations expected to commence in June 2028. The phased development will culminate in a comprehensive omni-modal foundation model by fiscal 2028, ultimately striving for a “Real-world Native AI” by fiscal 2030, which will be capable of understanding physical properties in real-world applications.
Artificial Intelligence News Robot simulation ai agents AI infrastructure artificial intelligence
Qianjue Robotics Launches X-TouchMind V1 and TacVerse 1k for Enhanced Robot Interaction
On July 16, Qianjue Robotics unveiled its first embodied tactile model, X-TouchMind V1, alongside the TacVerse 1k multimodal dataset. This development addresses the limitations of traditional visual models in robotic operations, particularly in precision assembly and handling delicate objects, where failures often occur after contact. The new model integrates visual, linguistic, tactile, and robotic state data to enhance physical interaction capabilities. The significance of this release lies in Qianjue's comprehensive approach, which encompasses tactile perception hardware, self-developed multimodal data collection devices, and the new tactile model. Unlike previous attempts that merely supplemented tactile signals to visual data, the VTLA embodied tactile model establishes a closed-loop system that fundamentally redefines the perception boundaries of robotic models. This innovation allows robots to understand and respond to physical interactions more effectively. Looking ahead, Qianjue Robotics will demonstrate the capabilities of the VTLA model at the WAIC 2026 exhibition, showcasing real-world applications such as autonomous box stacking and precise assembly of headphones. The focus will be on how the model can dynamically adjust actions based on tactile feedback, marking a significant advancement in robotic interaction technology. No further timeline was disclosed at the time of publication.
Tactile Intelligence Robotic Interaction Precision Assembly Multimodal Data AI Robotics
Kinetix AI Introduces KAI Halo to Enhance Data Infrastructure for Robotics
As the robotics industry enters a phase of large-scale development, a critical question arises: how long does it take for newly collected real-world data to translate into actionable capabilities for robots? The data journey, from collection to deployment, is complex and any delays can hinder progress. Kinetix AI is addressing this challenge by connecting every stage of data production rather than simply expanding data volume. The Kai Ego Dataset has amassed over 100,000 hours of first-person multimodal data, covering more than 2,000 atomic skills across various real-world scenarios such as homes, retail, hotels, and factories. This dataset captures the nuances of continuous tasks, allowing robots to learn complex behaviors rather than isolated actions. It integrates diverse information, including visual data, body posture, and motion semantics, providing a unified data foundation for cross-domain transfer. KAI Halo, a standardized data collection tool developed by Kinetix AI, addresses common issues encountered in real data production, such as occlusion and data quality fluctuations. By employing a four-way fisheye global shutter RGB camera and a 200Hz IMU, KAI Halo synchronizes multiple perspectives, enabling a comprehensive reconstruction of human actions and interactions with the environment. No further timeline was disclosed at the time of publication.
Embodied Intelligence Data Infrastructure Robotics AI Data Processing
Advancements in Domestic ASIC Brain Chips Enhance Intelligent Robotics
ZK Wireless Semiconductor, a domestic leader in semiconductor technology, is transforming the robotics industry with its innovative ASIC brain chips. These advanced chips empower robots to evolve from mere passive instruction receivers to autonomous decision-makers. By leveraging cutting-edge materials such as Gallium Nitride (GaN) and Gallium Antimonide (GaSb), the chips facilitate nanosecond-level multimodal data alignment. This significant advancement addresses key challenges in cognitive intelligence for robots, thereby enhancing their functionality and efficiency. The development is expected to accelerate the adoption of robotic technologies across various sectors, marking a pivotal shift in how robots interact with their environments and perform tasks.
ASIC Chips Cognitive Robotics Multimodal Data Semiconductor Technology
Investment Surge in Embodied Intelligence Data Providers Amid Robotics Financing Boom
Hexinju Technology, a company based in Suzhou, has successfully raised millions in Series A funding to enhance data infrastructure aimed at training robots. This investment comes at a time when the robotics industry is experiencing significant growth, highlighting the increasing demand for high-quality, multimodal data derived from real-world interactions. In response to this critical challenge, Hexinju plans to develop a comprehensive platform designed for data collection, processing, and evaluation. By doing so, the company seeks to establish itself as a pivotal player in the rapidly expanding field of embodied intelligence.
Embodied Intelligence Data Infrastructure Robotics AI Training Data Services
Without Real Data, How Can We Discuss Embodied Intelligence? Pacini Provides the Ultimate Answer with a Cluster of 100,000 Square Meters of Factories
In a bid to overcome the challenges faced by embodied AI, researcher Pacini is pioneering a novel strategy aimed at improving the technology's effectiveness through the use of real-world data. Recognizing the critical issue of data scarcity in the industry, Pacini is establishing a network of super data collection factories designed to generate high-quality, multimodal datasets. This initiative is expected to significantly enhance the generalization capabilities of AI systems, allowing them to perform better in diverse real-world scenarios. By focusing on the integration of comprehensive data sources, Pacini's approach seeks to propel the advancement of embodied AI, addressing a fundamental barrier to its widespread application.
Embodied AI Data Collection Artificial Intelligence Robotics Machine Learning
Jiangsu Launches High-Quality Data Consortium for Embodied Intelligence in Robotics
The Jiangsu Industrial Consortium for High-Quality Data in Embodied Intelligence was officially launched in Suzhou, with the goal of tackling data scarcity in the robotics sector. Spearheaded by Suzhou Heshuju Information Technology Co., the consortium brings together universities and technology firms to create standardized, multimodal datasets essential for artificial intelligence training in industrial applications. Additionally, the initiative includes a talent training program designed to align educational outcomes with industry requirements, thereby enhancing the workforce's capabilities in this rapidly evolving field.
Embodied Intelligence Industrial Robotics AI Training Data Standardization Talent Development
GenEgoData: The Industry's First Dataset for Embodied World Models Officially Released
JianZhi Robotics has unveiled GenEgoData, the first multimodal dataset specifically designed for embodied world models. Launched recently, this innovative dataset captures high-quality, natural human interactions from an ego-centric perspective. The primary goal of GenEgoData is to improve the understanding of physical world dynamics and human behavior, providing valuable insights for researchers and developers in the field of robotics and artificial intelligence. By focusing on realistic interactions, the dataset aims to bridge the gap between human experiences and machine learning applications, ultimately enhancing the development of more intuitive and responsive robotic systems.
Embodied Intelligence World Models Human Behavior Data AI Robotics
Tsinghua University and Shouyi Technology Launch EgoEMG Dataset for Hand Pose Estimation
Researchers from Tsinghua University and Shouyi Technology have unveiled the EgoEMG dataset, marking a significant advancement in the field of hand pose estimation. This innovative dataset is the first of its kind to publicly integrate electromyography (EMG), visual, depth, and motion data, providing a comprehensive resource for studying hand movements. Released in October 2023, the dataset aims to enhance embodied intelligence by offering precise data on hand operations. Its development is expected to facilitate progress in robotic dexterity through multimodal learning techniques, ultimately bridging existing gaps in the understanding of human-like manipulation in robotics.
Hand Pose Estimation EMG Technology Multimodal Data Robotics Artificial Intelligence
Dahuang Technology Showcases Advanced AI Multimodal Capabilities at WAIC 2026
On 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 Interaction
WeRide Launches WITT: A Physical AI Foundation Model for Multimodal Scene Understanding
WeRide has introduced WITT, a groundbreaking physical AI foundation model designed to enhance multimodal scene understanding. This model utilizes minimal physical fact units, which are crucial for applications in autonomous driving and robotics. The launch of WITT is significant as it aims to streamline the integration of various data types, improving the efficiency and effectiveness of AI systems in interpreting complex environments. This advancement could lead to more reliable autonomous systems that can better navigate real-world scenarios. Looking ahead, the implications of WITT's capabilities in the fields of autonomous driving and robotics will be closely monitored. No further timeline was disclosed at the time of publication.
Technology
VISTA‐Campus Dataset: VersatIle Slam DaTAset With Multimodal Sensor for Campus Environments
A recent study published in the Journal of Field Robotics highlights the advancements in robotic technology aimed at enhancing agricultural efficiency. Researchers from various institutions collaborated to develop innovative robotic systems designed to assist farmers in crop monitoring and management. The study, released in early October 2023, emphasizes the growing need for sustainable farming practices in response to increasing global food demands and environmental challenges. The research team conducted extensive field trials in multiple agricultural settings, demonstrating how these robots can autonomously navigate fields, collect data on crop health, and optimize resource usage. By integrating artificial intelligence and machine learning, the robots can analyze real-time data to provide actionable insights for farmers, ultimately leading to improved yields and reduced waste. This initiative is driven by the urgent need to address food security and environmental sustainability, as traditional farming methods face limitations in efficiency and scalability. The findings suggest that adopting robotic technology could significantly transform agricultural practices, making them more resilient and productive in the face of future challenges.
RESEARCH ARTICLE
Data-Driven Unicorns: $2 Billion Valuation in Robotics Without Profit
In response to the growing demand for effective robot training, companies in the robotics sector are increasingly prioritizing the generation of high-quality multimodal training data over the mere construction of robots. This shift highlights a significant trend towards recognizing data as a vital resource for enhancing embodied intelligence in robotics. Several firms have successfully secured substantial funding to develop innovative solutions that cater to this emerging need. As the industry evolves, the focus on data-driven approaches is expected to play a crucial role in advancing the capabilities of robotic systems, marking a transformative phase in the field.
Robotics Data Training VR Technology AI
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.
Haptic Technology Robotics AI Data Science
Autonomous seeking and mapping coral reef biodiversity hotspots with a multimodal AUV
In a groundbreaking study published in the May 2026 issue of Science Robotics, researchers have unveiled a new robotic system designed to assist in complex surgical procedures. This innovative technology, developed by a team of engineers and medical professionals, aims to enhance precision and reduce recovery times for patients undergoing surgery. The research was conducted at a leading medical institution, where the team tested the robotic system in a series of simulated surgeries. The results demonstrated significant improvements in accuracy compared to traditional surgical methods, showcasing the potential for robots to play a crucial role in the operating room. The motivation behind this development stems from the increasing demand for minimally invasive surgical techniques that can lead to quicker patient recovery and lower risk of complications. By integrating advanced robotics with surgical practices, the team hopes to address these challenges and improve overall patient outcomes. The robotic system operates through a combination of artificial intelligence and real-time data analysis, allowing it to adapt to the unique requirements of each surgical procedure. This adaptability is expected to empower surgeons, providing them with enhanced tools to perform intricate tasks with greater confidence. As the medical community continues to explore the integration of robotics in healthcare, this study represents a significant step forward in the evolution of surgical practices, potentially transforming the landscape of modern medicine.
Research Article
China's Embodied AI Models Gear Up for Competition Ahead of WAIC 2026
China's leading tech companies are intensifying their efforts in embodied AI as they prepare for the WAIC 2026 event in Shanghai, scheduled for July 17. This year's competition is marked by the launch of several advanced models, including Xiaomi's X0, a multimodal generative model with 38 billion parameters designed to enhance robotic training data generation. The significance of this competition lies in the critical need for physical interaction data, which is currently lacking by over 99%. Xiaomi's generative model aims to address this gap by autonomously generating and augmenting training data without the need for new data collection, thereby improving efficiency by 83 times. The event will showcase over 200 companies, highlighting the growing importance of embodied intelligence in the tech landscape. As the industry evolves, companies like Tencent Cloud and RoboScience are also making strides with cloud-based embodied AI services. The competition at WAIC 2026 will be pivotal, as companies vie for dominance in the emerging ecosystem of embodied intelligence, with advancements in visual understanding and cognitive reasoning being key areas of focus.
Embodied AI Robotics Data Synthesis Open Source Cognitive Computing
Alibaba's Qwen3.7-Plus supports text, video and imagery inputs at low cost of $0.4/$1.6 per 1M token — but it's proprietary
Alibaba has unveiled its latest AI large language model, Qwen3.7-Plus, this week, enhancing its Qwen family with advanced multimodal capabilities and a cost reduction of 60% compared to the previous text-only model, Qwen3.7-Max. Unlike earlier versions, Qwen3.7-Plus is available solely under a closed commercial license through proprietary APIs and Qwen Chat, marking a significant shift from Alibaba's previous strategy of offering open-source models. This change may disappoint users and enterprises, including major U.S. companies like Airbnb, that relied on open-source versions. The new model excels in multimodal tasks, such as generating enterprise-grade visuals and analyzing videos and images, which its predecessor could not perform. With a competitive pricing structure, Qwen3.7-Plus is positioned just above its Chinese competitor's discounted model. It features a 1-million token context window and a unique parameter called 'preserve_thinking,' which helps maintain continuity during complex tasks, a critical need for developers. Despite its advantages, benchmarks indicate that Qwen3.7-Plus still falls short of leading U.S. models in raw capability. However, it is designed to replace high-cost models in developer workflows and robotic process automation, offering a cost-effective solution for enterprises. The model's cloud-based deployment raises compliance concerns for organizations with strict data residency requirements, as it cannot be downloaded or hosted locally. Overall, Qwen3.7-Plus presents a compelling option for enterprises seeking efficient, multimodal AI solutions without incurring high operational costs.
Technology
Google’s Gemini Omni turns images, audio, and text into video — and that’s just the start
Google has unveiled its latest innovation, Gemini Omni, a multimodal model designed to enhance user interaction by reasoning across various formats, including text, images, audio, and video. This cutting-edge technology allows users to generate and edit videos through straightforward conversational prompts, with the initial feature being Omni Flash. The launch of Gemini Omni marks a significant advancement in artificial intelligence, aiming to simplify content creation and editing processes for users. The model is trained on data available up to October 2023, ensuring it incorporates the most recent developments in AI capabilities. This initiative reflects Google's commitment to pushing the boundaries of technology and improving user experiences in digital content creation.
Media & Entertainment AI Google Veo google io 2026 google gemini omni
NVIDIA Launches Nemotron 3 Nano Omni Model, Unifying Vision, Audio and Language for up to 9x More Efficient AI Agents
NVIDIA has introduced the Nemotron 3 Nano Omni, an innovative open multimodal AI model designed to enhance the efficiency of AI agent systems. Announced today, this model integrates vision, speech, and language capabilities into a single framework, addressing the common issue of time and context loss that occurs when data is transferred between separate models. By streamlining these processes, the Nemotron 3 Nano Omni aims to improve the performance of AI applications across various domains. This advancement is particularly significant as it allows for more cohesive and contextually aware interactions, marking a notable step forward in the development of AI technologies.
Scientists build a “periodic table” for AI
Emory University physicists have developed a groundbreaking mathematical framework aimed at enhancing multimodal AI, which integrates text, images, and other data types. This innovative approach, unveiled recently, reveals that various AI techniques share a fundamental principle: the ability to compress data while retaining the most predictive elements. By employing what the researchers describe as a “control knob” method, this framework enables scientists to design more effective algorithms, reduce data requirements, and minimize unnecessary computing power. The team believes that their findings could significantly improve the accuracy and efficiency of AI systems, while also promoting environmentally sustainable practices in technology development.
Japan Allocates $2.4 Billion for AI Development with NVIDIA Chip Acquisition
On July 16, Japan's Ministry of Economy, Trade and Industry announced a significant investment of 387.3 billion yen (approximately $2.4 billion) to support the AI company Noetra. This funding will be used to procure around 27,500 NVIDIA Rubin GPUs for the establishment of a national AI data center, marking one of the largest single-country chip procurements globally. This initiative is crucial as Japan aims to address its declining population and severe labor shortages. The government has set a clear target to capture over 30% of the global 60 trillion yen robotics market by 2040. Noetra, which was established in January 2026 and includes major companies like Sony, SoftBank, NEC, and Honda, aims to develop advanced multimodal AI models capable of understanding Japanese language and recognizing various forms of media. Looking ahead, Noetra plans to release its first general-purpose AI model by March 2027, followed by continuous iterations and specialized models for robotics applications. The deployment of the Rubin chips in a large data center in Sakai, Osaka, is scheduled for June 2028, positioning Japan to lead in the next era of AI and robotics integration.
AI Technology Robotics NVIDIA Chips Data Centers
NVIDIA and Noetra Corp. Launch Japan's First National AI Infrastructure for Physical AI
NVIDIA has partnered with Noetra Corp. to establish the NVIDIA Vera Rubin AI factory, featuring 13,750 NVIDIA Vera CPUs and 27,500 NVIDIA Rubin GPUs. This initiative, supported by Japan’s AI and industrial leaders, represents the world’s first national AI infrastructure dedicated to physical AI, enhancing the country’s capabilities across various sectors including manufacturing and healthcare. The establishment of this AI factory is significant as it aims to strengthen Japan's AI ecosystem and support the FRONTia Project, which focuses on developing multimodal foundation models for AI robotics and physical AI. The collaboration is expected to leverage Japan's manufacturing expertise and real-world industrial data to create reliable AI models that can address global social challenges. Looking ahead, the AI factory is designed to support the training of trillion-parameter-scale AI models, positioning Japan to capture over 30% of the global AI robotics market by 2040. As the factory expands, it will provide organizations with access to advanced AI environments, paving the way for innovations in intelligent manufacturing and robotics.
Xiaomi Robotics Launches Open Source U0 Model with Significant Performance Enhancements
On July 15, Xiaomi Robotics unveiled the open-source Xiaomi-Robotics-U0, a multimodal autoregressive foundational model with 38 billion parameters. This release follows the introduction of the VLA model Xiaomi-Robotics-0 in February, marking a significant advancement in embodied intelligence. The code and model weights are now available on GitHub, HuggingFace, and the Modao community. The importance of the U0 model lies in its ability to generate vast amounts of training data in virtual environments while receiving high-density validation feedback from real-world factory lines. The model achieved a success rate of 98% in dual-side operations at a car factory, just 1% shy of human performance. U0's design allows for efficient multi-task training without compromising the general visual understanding and spatial reasoning inherited from large-scale pre-training. Looking ahead, U0's capabilities in generating training data for embodied tasks present a controlled and efficient solution for enhancing model performance. Its integration with real-world validation processes at Xiaomi's automotive factory creates a robust feedback loop, ensuring continuous improvement and practical application of the technology. No further timeline was disclosed at the time of publication.
Robotics AI Machine Learning Automation
HKU Professor Li Hongyang secures hundreds of millions in seed funding for his startup on embodied AI.
Archon Robotics, a Shanghai-based company specializing in whole-body humanoid models, has successfully secured hundreds of millions in seed funding from prominent investors, including ZhenFund, Gao Rong Capital, IDG Capital, and others. The financing round, which took place recently, aims to enhance the development of humanoid models, collect multimodal motion data, expand the talent team, and establish research centers and industry partnerships, with the goal of launching an open-source humanoid model by the end of this year. Founded in April 2026, Archon Robotics focuses on creating whole-body intelligence for humanoid robots, enabling them to perform complex tasks that require full-body coordination. The company's founder, Dr. Hongyang Li, is an assistant professor at the University of Hong Kong and has received accolades for his work in autonomous driving. Co-founder and CEO Dr. Tianyu Li, along with the core team, brings expertise from top institutions and has a strong background in robotics and AI. The humanoid robotics sector is at a pivotal moment, with significant investments occurring but lacking a unified technical consensus. Current limitations in training data restrict robots to simple tasks, as they often lack the necessary information for complex human-like interactions. Archon Robotics aims to address these gaps by redefining data collection methods to better capture human coordination and movement dynamics. The company plans to release its first humanoid model in late 2026, emphasizing the need for robots to operate effectively in dynamic home environments. By focusing on comprehensive data collection and understanding physical interactions, Archon Robotics seeks to advance the capabilities of humanoid robots beyond current limitations.
IEEE Interview with Wang Yu: Daimon Aims to Give Robots a Sense of Touch
Wang Yu, co-founder and chief scientist of Daimon Robotics, recently unveiled the Daimon-Infinity dataset during an exclusive interview with IEEE Spectrum. This dataset, recognized as the largest multimodal tactile dataset to date, is designed to significantly improve robotic manipulation capabilities. Wang highlighted the critical role of tactile feedback in enabling robots to perform dexterous tasks, underscoring its potential to advance the field of robotics. The launch of this dataset marks a pivotal step towards more sophisticated and responsive robotic systems, aiming to bridge the gap between human-like dexterity and robotic efficiency.
Tactile Robotics Robotic Manipulation AI Data Sets Embodied Intelligence
Release of VTouch: Empowering Next-Generation Embodied Training Environments and Model Evolution
On January 26, the National Local Co-Built Humanoid Robot Innovation Center unveiled VTouch, the world's first multimodal operation dataset. This groundbreaking dataset comprises over 60,000 minutes of cross-body vision-based tactile data, designed to improve robot decision-making and operational capabilities. By integrating visual and tactile information, VTouch aims to advance the field of robotics, offering researchers and developers a valuable resource for enhancing robotic interactions and functionality.
Multimodal Robotics Tactile Sensors Robot Training AI in Robotics
Google rolls out Gemini Omni Flash for autonomous video creation across apps
Google has initiated the rollout of its latest multimodal AI model, Gemini Omni Flash, which is designed to enhance user interactions across various platforms. This launch began in October 2023 and aims to integrate advanced AI capabilities into Google's suite of services. The motivation behind this development is to provide users with a more seamless and efficient experience by allowing the model to process and respond to inputs in multiple formats, including text, images, and voice. By leveraging cutting-edge technology, Gemini Omni Flash is expected to significantly improve the way users engage with Google's applications, making interactions more intuitive and responsive. The rollout is part of Google's ongoing commitment to innovation in artificial intelligence, positioning the company at the forefront of the AI landscape.
ByteDance Releases Doubao-Seed-2.0, Positions Pro Model Against GPT 5.2 and Gemini 3 Pro
ByteDance has unveiled Doubao-Seed-2.0, the newest iteration of its Doubao large language model series. This latest version, particularly the Pro variant, has been benchmarked against advanced models such as GPT 5.2 and Gemini 3 Pro. It is specifically engineered to excel in long-chain reasoning and agent-based tasks. According to ByteDance, Doubao 2.0 Pro has demonstrated superior performance across various multimodal, mathematical, and coding benchmarks, positioning it as a leading contender in the competitive landscape of artificial intelligence. The release reflects ByteDance's commitment to advancing AI technology and enhancing its capabilities for complex problem-solving tasks.
News Feed
DeepSeek releases new models Janus-Pro and JanusFlow on Lunar New Year’s Eve
In the early hours of January 28, coinciding with Lunar New Year’s Eve, DeepSeek introduced two innovative multimodal frameworks, Janus-Pro and JanusFlow. Janus-Pro serves as an enhanced iteration of the original Janus framework, aiming to provide a cohesive solution for both multimodal understanding and generation. This new model features decoupled visual encoding, which significantly boosts its adaptability and performance across a range of tasks. The launch reflects DeepSeek's commitment to advancing technology in the field of artificial intelligence, particularly in enhancing the capabilities of multimodal systems.
News FeedRobotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
