Bulgarian AI model SPEAR-1 gives robots 3D vision, open source

🤖 A $300 robot that matches cutting-edge performance? Yes, really. Meet EverydayVLA—a 6-DOF manipulator that combines affordable hardware with state-of-the-art vision-language-action AI. Researchers built a unified model that outputs both discrete and continuous actions, plus an adaptive-horizon ensemble that monitors uncertainty and re-plans on the fly for safer, more reliable manipulation. The results? On real-world tasks, it beats prior methods by 49% in familiar scenarios and 34.9% in novel, cluttered environments. • Why it matters: Robotics accessibility just got a major upgrade. Low-cost, high-performance systems like this could democratize robot learning for homes, labs, and startups worldwide. Follow us for more breakthroughs in robotics and AI! 🚀 #Robotics #VisionLanguageAction #RoboticManipulation #AI #RoboticsResearch #Automation Source: https://lnkd.in/gBSXCGux

European researchers just released something that changes everything for factory robots. Open source. Free to download. It thinks in three dimensions. Meet SPEAR-1. The AI brain that gives robots unprecedented dexterity. Developed at Bulgaria's INSAIT, this foundation model stands apart. It trains on 3D data. Robots now understand physical space like never before. The results? Performance matching billion-dollar commercial systems. This matters because: 🤖 Current robots need complete retraining for new environments 🔧 Setup costs remain prohibitively high for many businesses ⚡ Programming complexity limits widespread adoption SPEAR-1 changes the game. Open source means faster innovation. More researchers can experiment. Startups can iterate rapidly. We're seeing the same democratization that happened with language models. But this time, it's physical intelligence. The robotics industry is still early. Most systems can't generalize across tasks. But massive training data plus computing power is showing promise. As one researcher noted: "Building general robotic systems was not possible even a year ago." Today, it's reality. The implications extend beyond manufacturing. Logistics, construction, healthcare - all could benefit from accessible robot intelligence. When advanced AI becomes open source, entire industries transform. What sectors do you think will adopt AI-powered robotics first? #Robotics #AI #OpenSource 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gv4TxyRv

🤖 Amazon and Carnegie Mellon University just joined forces to launch an AI Innovation Hub—and it's a game-changer for robotics. The partnership combines Amazon's real-world automation expertise with CMU's world-class AI research capabilities. The hub will focus on leveraging both institutions' strengths in artificial intelligence and robotics to drive breakthrough innovations. • Why it matters: This collaboration bridges academia and industry, accelerating the development of smarter, more capable robotic systems that could reshape how we work and automate complex tasks. Follow for more updates on AI, robotics, and the future of intelligent automation! 🚀 #Robotics #ArtificialIntelligence #Innovation #AmazonRobotics #CarnegieMellon #TechPartnership #FutureOfWork Source: https://lnkd.in/gdjj3-eJ

🤖 What if robots could learn to recover from mistakes mid-task without retraining? That's exactly what researchers just achieved. Meet MoE-DP: a new approach that combines Mixture of Experts with diffusion policies to make robotic manipulation more robust and interpretable. The breakthrough? By decomposing complex tasks into specialized "expert" modules, the system can handle long-horizon manipulation with real failure recovery. In tests, it delivered a 36% improvement in success rates under disturbance—and it works in the real world too. • Why it matters: Robots that can learn semantic task primitives (approaching, grasping, etc.) and dynamically adapt represent a major leap toward practical, resilient automation. Follow for more breakthroughs in robotics and AI! 🚀 #Robotics #AI #MachineLearning #DiffusionModels #Automation #RoboticManipulation Source: https://lnkd.in/g-CnTMUR

Today I’m digging into trends happening around Physical AI and I’ve spotted three major waves shaping its evolution, from the past to the near future. Wave 1: The Full-Stack Era (2015–2021) This was the “do it all” age. Startups built everything from scratch. Companies like Attabotics, Starsky Robotics, and Argo AI burned through hundreds of millions trying to own the entire stack. It was visionary… …but unsustainable. These companies weren’t just building robots, they were, unknowingly, building the infrastructure for robotics. Wave 2: The Unbundling Era (2022–2025) Investors and founders realized that the biggest opportunities weren’t in the robots themselves, they were in the “picks and shovels” that make robots possible. This triggered the great unbundling, an explosion of tooling and middleware startups, each mastering one piece of the puzzle: Formant: “Datadog for robots” (fleet observability) Foxglove: “Visual Studio for robotics developers” (data visualization) Polymath Robotics: “Stripe for autonomy” (API-driven control stacks) PickNik Robotics: “Red Hat for manipulation” (open-core model) This era transformed robotics from hardware-heavy R&D into something modular, API-driven, and capital-light. Wave 3: The Networked Intelligence Era (2026–2030) Now comes the next transformation, when robots stop learning alone and start learning together. Physical AI systems will soon operate as connected intelligence networks: fleets, digital twins, and AI models sharing knowledge through the cloud. Picture this: A delivery robot in Toronto hits black ice and learns to recover that experience instantly updates the driving models of fleets in Stockholm. A robotic arm in Japan learns a new grasp technique and uploads it to a global “robot skill store” that any factory in the world can download. Fleets across industries share model updates securely through federated learning, improving global performance without exposing raw data. This is the Internet moment for robotics. Just as the Internet connected people, and cloud computing connected software,I think Wave 3 connects embodied intelligence: robots, vehicles, and environments that continuously improve each other through data loops. The future of Physical AI isn’t another humanoid. It’s the network where all robots think, learn, and evolve together. #PhysicalAI #Robotics #AIInfrastructure #Tooling #RobotOps #EdgeAI #FutureOfAI #DeepTech

🤖 𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝗿𝗼𝗯𝗼𝘁𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗯𝘆 𝘁𝗼𝘂𝗰𝗵, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗯𝘆 𝘀𝗲𝗲𝗶𝗻𝗴. 𝗔𝗜 𝗶𝘀 𝘁𝗮𝗸𝗶𝗻𝗴 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗹𝗲𝗮𝗽 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗹𝗱. Generalist AI has just introduced 𝗚𝗘𝗡-θ — 𝗮 𝗴𝗿𝗼𝘂𝗻𝗱𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 designed for 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗻 𝗿𝗲𝗮𝗹 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀. No more relying only on simulations — this model learns from 𝗵𝗶𝗴𝗵-𝗳𝗶𝗱𝗲𝗹𝗶𝘁𝘆 𝗿𝗼𝗯𝗼𝘁𝗶𝗰 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. That means: 🤝 𝗥𝗶𝗰𝗵𝗲𝗿 𝗽𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 (it doesn’t just see, it feels). ⚙️ 𝗕𝗲𝘁𝘁𝗲𝗿 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 between sensing, reasoning, and action. 🚀 𝗙𝗮𝘀𝘁𝗲𝗿 𝗮𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 to unpredictable, real environments. Think of it as AI moving from “understanding” the world to 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹𝗹𝘆 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗶𝘁. GEN-θ could become the blueprint for 𝗿𝗼𝗯𝗼𝘁𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗯𝘆 𝗱𝗼𝗶𝗻𝗴 — 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. This is a massive step toward 𝘁𝗿𝘂𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝗹-𝗽𝘂𝗿𝗽𝗼𝘀𝗲 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀, where models evolve not from static data but from 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. 💭 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲? Will embodied learning redefine how AI interacts with the real world — or will hardware still be the biggest challenge? Let’s talk 👇 #EmbodiedAI #GeneralistAI #AIFuture #FoundationModels #TechInnovation

2025: The Breakthrough Year of Humanoid Robots and Behavioral AI As 2025 draws to a close, it’s clear that this year has marked a turning point for humanoid robotics — when AI began to move beyond logic into emotion, behavior, and real-world adaptability. What once were mechanical prototypes have evolved into robots capable of learning from human interaction, driven by the rise of Behavioral AI. The new generation of humanoid robots focuses not only on movement or mechanics, but on behavioral intelligence — the ability to interpret tone, emotion, and micro-expressions to respond naturally in human environments. With multi-layer sensing systems and adaptive AI models, robots are no longer limited to following commands; they now perceive and interpret human context. From Tesla’s Optimus and Agility Robotics’ Digit to emerging humanoid projects in Japan, China, and the U.S., 2025 has shown the world that robots can coexist with humans — not just as tools, but as collaborative partners. They are entering healthcare, education, and customer service, expanding far beyond the assembly line. For industries, this signals the rise of cognitive automation — where machines combine analytical precision with social awareness. Yet, this also brings new discussions around AI ethics, data privacy, and the moral boundaries of machine consciousness, shaping the future dialogue between humans and intelligent systems. As we move toward 2026, one thing is certain: robots are no longer just learning how to work — they’re learning how to connect. 🌐 Fukuda.ai – Empowering businesses with cutting-edge automation, AI, and intelligent robotics solutions. We partner with enterprises to drive innovation, digital transformation, and next-generation productivity. ☎️ Call: +1 714-612-9382 📧 Email: leo.nguyen@fukuda.ai 🏢 12822 Joy St., Garden Grove, CA 92840 USA 🌐 Web: Fukuda.ai #HumanoidRobot #BehavioralAI #FukudaAI #AI2025 #AI2026 #RoboticsInnovation #ArtificialIntelligence #HumanRobotInteraction #Automation #CognitiveAI #SmartTechnology #IndustrialAutomation #VietnamTech #FutureOfWork #ロボティクス #未来技術

🤖 How Robots Learn — Machine Learning Explained! Ever wondered how robots recognize objects or make smart decisions? That’s the power of Machine Learning (ML) — a core branch of Artificial Intelligence that enables machines to learn from data instead of being explicitly programmed. By feeding data and patterns, robots and AI systems can improve performance over time — much like humans learning from experience. At Global Robotics & AI Research Company, we’re committed to advancing research in intelligent robotics powered by AI and ML — building smarter systems that think, adapt, and evolve. ⚙️✨ #MachineLearning #ArtificialIntelligence #Robotics #AIResearch #GlobalRobotics #TechEducation #Automation #Innovation #AIForGood #STEM

AI and robotics evolve fast. This curated Start.me page brings together the most relevant tools, resources, and updates—all in one organized hub. Perfect for researchers, developers, and innovators who want to stay current. https://lnkd.in/evq2DmJp #AI #Robotics #Innovation

What began as a viral meme a robot clicking “I’m not a robot” has now become a striking symbol of our era. Recent advancements in artificial intelligence and robotics have blurred the boundaries between human cognition and machine automation. This week, researchers at MIT and OpenAI showcased a new class of autonomous agents capable of self-verification systems that can assess their own operational status and decision logic without human input. The demonstration sparked a wave of philosophical debate online: when a machine affirms its own identity, is it simply executing code, or is it crossing into self-awareness? Experts suggest that while we’re far from creating machines with consciousness, these developments mark a turning point in how AI perceives, interacts with, and interprets the world. The question is no longer just about capability it’s about comprehension. We are entering an age where tools are no longer extensions of human will, but reflections of it. And that demands not fear, but deep reflection and ethical foresight. #artificialintelligence #aiethics #robotics #automation #machinelearning #futureofai #technologynews #innovation