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Industrial robots used to need complete retraining for new tasks. This open-source AI model learns once, works everywhere. Physical Intelligence spent billions. INSAIT made it free. SPEAR-1 changes everything. This Bulgarian breakthrough gives robots 3D vision. They understand space like never before. The model matches billion-dollar commercial systems. But it's completely open source. Why this matters: • Startups can experiment without massive budgets • Researchers accelerate progress together • Small manufacturers access advanced robotics • Innovation happens faster across the board Robot intelligence was stuck. Each new arm meant starting over. Each environment required retraining. Now we're applying the language model approach. Massive data plus computing power equals general capabilities. What seemed impossible a year ago is happening today. The democratization of robotics has begun. Open source is leveling the playing field. Just like open language models transformed AI. What industry will benefit most from accessible robot intelligence? #OpenSource #Robotics #IndustrialAutomation 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gk7w2G98

At Botinfo.ai, we study how machines are crossing the boundary between software and embodiment — evolving from digital assistants into intelligent, physical agents that can perceive, reason, and act in the real world. Over the past decade, AI models like GPT-4, Gemini, and Claude have shown us that machines can think. Now humanoid robots from Unitree, Figure AI, Tesla, Agility Robotics, and Sanctuary AI are showing that machines can move, adapt, and learn from the physical world — a leap that brings us closer to artificial general intelligence (AGI). Why does this matter? Because true intelligence isn’t just cognitive — it’s embodied. A system that can reason and interact with its environment learns faster, generalizes better, and develops emergent problem-solving abilities. This is the foundation of what researchers call “embodied AI” — the bridge between current AI and the eventual rise of physical general intelligence. At Botinfo.ai, we document this transformation through structured insights, comparisons, and real demonstrations — both online and on our YouTube channel @botinfoai . Explore our coverage and see how humanoid robotics is shaping the next frontier of AI: 👉 https://botinfo.ai #HumanoidRobotics #EmbodiedAI #ArtificialGeneralIntelligence #Robotics #AI #Botinfoai

🤖 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

🤖 Robots just got better at understanding what you want them to do. Here's the breakthrough: Meet TRACE—a new method that teaches vision-language models to "think out loud" before manipulating objects. Instead of jumping straight to coordinates, the model reasons through instructions step-by-step in plain text first. The result? State-of-the-art accuracy on robotic placement tasks (48.1% on the Where2Place benchmark—a 9.6% jump). The key insight: textual reasoning is computationally efficient and surprisingly interpretable. • Why it matters: Clearer AI reasoning = more reliable, precise robot control in real-world applications—from manufacturing to logistics. Follow us for more breakthroughs in robotics and AI innovation! 🚀 #Robotics #VisionLanguageModels #AI #RoboticManipulation #MachineLearning #ComputerVision Source: https://lnkd.in/gYjr6hAy

Imagine a robot that learns just by watching you move. 🤖⁣⁣ ⁣⁣ Meet Neo by 1X — the humanoid robot that learns by observing humans, not by endless coding. With embodied AI, Neo understands movement, picks up objects, and improves through real-world experience — just like us.⁣⁣ ⁣⁣ From its soft tendon-driven body to its quiet 22-decibel operation, Neo is proof that the future of robotics is already here.⁣⁣ ⁣⁣ Want to start building your own innovations in AI, robotics, and space tech? 🌟⁣⁣ ⁣⁣ Learn more:⁣⁣  👉 camp.integem.com⁣⁣  📞 +1-408-459-0657⁣⁣ ⁣⁣ #STEM #AI #Robotics #spacetech #privatetutor #highschool #kto12 #engineering #educationalvideo⁣⁣ ⁣ ⁣ ⁣ ⁣

Carl Jung's Cognitive Functions for ROBOTICS?! Headline: Ever wondered how AI 'thinks' or 'perceives'? We reimagined Carl Jung's Cognitive Functions for the world of Robotics! It's fascinating to draw parallels between human psychology and artificial intelligence. This graphic takes Jung's well-known "Cognitive Functions" and creatively reframes them to describe Robotic Control & Perception Functions, based on Modular AI Principles. Just as humans use different cognitive functions to interact with the world, robots employ distinct modules for: Extroverted Functions (Sensors & Actuators): How robots gather information from their environment (like Vision (Cameras)) and perform actions (Motor Control). Introverted Functions (Processing & Cognition): How robots process that information internally (like SLAM (Mapping)) and make decisions (Path Planning, Decision Making, Object Recognition). This playful yet insightful analogy helps us visualize the complex interplay between hardware, software, and intelligence that makes autonomous systems possible. It highlights how robots: Sense: Through various sensors, gathering "experiences." Perceive: Building internal models of their environment. Think: Planning actions and making decisions. Act: Executing commands to achieve goals. What are your thoughts on applying psychological frameworks to AI and robotics? Do you see other human cognitive functions mirrored in robotic design? #Robotics #AI #ArtificialIntelligence #CognitiveFunctions #CarlJung #MachineLearning #Automation #Innovation #Tech #intertech #jassim

💡 *Robot “Emotions” vs Human Feelings: Can Your Robotic Family Member Really Feel?* 💡 As artificial intelligence and humanoid robots evolve, we naturally wonder — can they really feel emotions, or only simulate them? Research in human–robot interaction shows that people often respond empathetically toward robots, even feeling sorry when a robot appears hurt. Yet these reactions come from human perception, not from any real feeling within the machine. Robots may mimic emotion through programmed expressions or voice tones, but they lack the biological and conscious processes that make human emotions genuine. Interestingly, studies show that when robots display highly human-like emotions, people sometimes feel discomfort or mistrust — a reminder that emotional simulation can never fully replace true empathy or awareness. While we can develop bonds with robots that look or act like family members, these are one-way emotional connections. The robot reflects or reacts; the human truly feels, learns, and grows. As we enter an age of AI companions and robotic colleagues, the challenge isn’t to replace emotion — but to design technology that enhances, respects, and strengthens our human emotional intelligence. #AI #Robotics #EmotionalIntelligence #HumanRobotInteraction #Technology #Innovation #FutureOfWork

🚀 Is this how we finally get robots to master the real world? One of the biggest hurdles in robotics has always been the "sim-to-real" gap. Training a robot in the real world is incredibly slow, expensive, and risky (imagine a robot learning to use your fine china!). Virtual simulations are safer, but often too simple or unrealistic for a robot to transfer its learned skills to our messy, unpredictable homes. That's why I was so fascinated by this new tool from MIT CSAIL, called Steerable Scene Generation. It's a genuine game-changer. In a nutshell, it uses a powerful combination of generative AI (a steered diffusion model) and the same strategic search technique that powered AlphaGo (Monte Carlo Tree Search) to build incredibly realistic and physically accurate 3D worlds. But here's the magic ✨: The AI doesn't just create random scenes. It "steers" the creation process to build logical, everyday environments. It knows that forks and plates go on a table, not through it. This allows a robot to practice complex tasks, like setting a table or stacking dishes, endlessly in a safe, scalable virtual world. This is more than just a cool simulation. It's a potential solution to the data bottleneck that has held back the development of general-purpose robots. By making training faster, cheaper, and far more realistic, we're taking a massive leap toward robots that can finally navigate and interact with our world safely and effectively. To me, this feels like a foundational piece of the puzzle falling into place. We're not just building robots; we're building intelligent, dynamic worlds to teach them. What's the first task you'd want a household robot to learn? I'd love to hear your thoughts! Credits: MIT CSAIL Nicholas Ezra Pfaff #AI #Robotics #Innovation #MIT #CSAIL #MachineLearning #GenerativeAI #FutureOfTech #ArtificialIntelligence

AI + Computer Vision are giving robots the ability to perceive, understand, and act — just like humans. Through 3D vision, depth sensing, and reinforcement learning, robots can now navigate, recognize, and interact intelligently in real time. 💡 The future of robotics isn’t just automation — it’s autonomy. At INVAYL, we’re building toward machines that learn from perception, not programming. 🚀 #Invayl #AI #ArtificialIntelligence #Robotics #Automation #Innovation #DeepLearning #MachineLearning #AIRevolution #Tech #Future #SmartTechnology #ComputerVision #AI2025 #InnovationNation Revolution.AI Generative AI