Google is Racing Ahead in AI — Daily Innovations and Breakthroughs!” Google has introduced Gemini Robotics-ER 1.5 (preview), bringing advanced vision-language capabilities to robotics. The model can detect and track objects in images and videos, understand audio and language instructions, and then translate them into actionable steps for robots. This opens possibilities for smarter automation, real-time video analysis, and more natural human-robot interaction — from warehouses to healthcare and beyond. 👉 Learn more:https://lnkd.in/ga3dKWmu #GoogleAI #GeminiAI #Robotics #VisionAI #ObjectDetection #MultimodalAI #AIInnovation #MachineLearning #Automation #ArtificialIntelligence #NextGenAI #AIUpdates #TechTrends

Robots are now operating with a level of precision that doesn’t even feel real. 1) Solved Rubik’s Cube in 0.103 seconds (faster than the blink of an eye). 2) Another delicately opened the cover of an egg without breaking it. 3) Mimicking human expressions so accurately they look real All this isn’t just about cool demos. It’s about what happens when machines achieve superhuman control: • In construction → robots laying bricks & glass with millimeter accuracy • In healthcare → surgical systems performing tasks steadier than human hands • In design → humanoid robots bridging interaction between humans and AI But here's the thing: precision at this level requires instant decision-making. Even 100ms of cloud latency can turn precision into failure. At Solo Tech, we help companies deploy AI directly on their robots, achieving sub-10ms response times that make superhuman precision possible. Access Physical Intelligence Now → getsolo.tech #GetSoloTech #PhysicalAI #OwnYourAI #Robotics #PrecisionEngineering

The robotics field is at an inflection point. Classical techniques got us safety and remote assist. Traditional ML pushed mobility close to human-like levels with balance and arm movement. But the real game-changer: Foundation models. By learning patterns across tasks and modalities, they’re unlocking dexterity, perception, and human–robot interfaces in ways we’ve never seen before. Imagine robots responding seamlessly to visual inputs and spoken commands — moving from specialized tools to general-purpose collaborators. The future of robotics is not just about mimicking humans, but exceeding our capabilities in entirely new ways. The source of the image is #Mckinsey. Are we on the verge of the “iPhone moment” for robotics? #Robotics #AI #FoundationModels #FutureOfWork #Automation

ABB Robotics and LandingAI unleash the power of generative AI for Robotic vision with a strategic investment in California-based LandingAI, co-founded by Andrew Ng, Stanford professor and one of the world’s leading voices in AI. Together, we’re integrating their Large Vision Model, LendingLens™, into our software suite – marking a major step forward in our vision for Autonomous Versatile Robotics – AVR™. The first-of-its-kind collaboration will: • Reduce vision training and deployment time by up to 80% • Make robot vision easier to use and deploy through no-code tools and pre-trained models • Strengthen our AI capabilities across platforms like RobotStudio®, making industrial-grade automation accessible for a broader range of users. Read more here: https://lnkd.in/eEgwWg-w #AI #Robotics #Robots #Cobots #VisionSystem #LargeVisionModel

From a robotics perspective, I explored whether a robot could use visual data from a motion capture (mocap) suit to learn dexterity in movement. The idea was to capture the subtle, fluid motions of a human performing complex tasks and translate them into robotic control signals. In theory, this could allow machines to mimic human adaptability, learning not just rigid sequences of motion, but context-sensitive adjustments that respond to the environment in real time. However, this highlighted a key limitation: while robots can replicate patterns, they struggle with the unpredictable, improvisational aspects of human dexterity. Small deviations, unexpected obstacles, or novel situations quickly expose the rigidity of current AI systems. What this demonstrates is that dexterity is more than a series of motions, it is judgment, anticipation, and the ability to adapt on the fly. Translating that into AI-controlled systems remains a frontier that is far from solved. #Robotics #ArtificialIntelligence #MotionCapture #Dexterity #AIResearch #Automation #MachineLearning #AIInfrastructure #FutureOfWork #TechInnovation #DigitalTransformation

🤖 MIT CSAIL researchers have introduced Neural Jacobian Fields, a system that lets robots learn how their bodies respond to commands using only a single camera. Instead of programming robots line by line, this approach feels much closer to how we learn: by experimenting, observing, and adapting. It means more flexible, affordable, and self-aware robots capable of working in environments where traditional control systems fail. Source: https://lnkd.in/ewnJG9Et #Robotics #AI #ComputerVision #Innovation #Automation #MachineLearning #MIT

Humanoid robots in the home? "Not so fast." Google DeepMind’s latest showcase of Apptronik’s Apollo folding laundry and sorting recycling made big headlines last week. The demos — powered by the new Gemini Robotics 1.5 models, hint at a future where we might ask a robot to tidy up with just a few words. But as Northeastern professor Ravinder Dahiya points out, we’re still a long way from true household humanoids. What looks like “thinking” is really structured algorithms plus vast training data. These robots don’t understand the world, they operate within carefully bounded rules. Missing are the subtle senses we rely on every day: touch, pain, even smell. Dahiya’s work on electronic skins shows just how much is left to do before robots can manipulate the fragile, irregular, and unpredictable textures of human life. At Res Machina, we see this not as a limit but as a reminder: living with machines isn’t about perfect replacements for human capability. It’s about how technology reshapes our routines, our expectations, and our sense of presence. The road to humanoids in the home will be long, and full of quiet changes in how we imagine what robots are for. #robotics #AI #humanoids #ResMachina https://lnkd.in/e7sbkX-b

Memory turns a robot from a tool into a teammate. When robots remember where they’ve been, what worked, and why, they explain decisions, learn faster, and help safely in the real world—classrooms, clinics, warehouses, and homes. That’s Memory Robotics: building episodic memories of lived experience and semantic memories of concepts so machines can recall, reason, and improve—not just repeat. Recent work shows robots verbalizing their own past actions from lifelong logs, knowledge frameworks (like KnowRob/RoboBrain) structuring long-term facts, and new episodic memory banks that boost lifelong learning and rapid adaptation. This isn’t science fiction—it’s a practical path to trustworthy, auditable autonomy. Speaker Dr. Qamar Ul Islam D.Engg. B.Tech. M.Tech. Ph.D. FHEA Artificial Intelligence Robotics #MemoryRobotics #EpisodicMemory #KnowRob #RoboBrain #LifelongLearning #ExplainableAI #Robotics #EdgeAI #HumanRobotInteraction #InfiniteMind #LinkedIn

"The Future of Artificial Intelligence Machines" 🤖 Discover AiM Future’s Multimodal AI Demo — designed for Smart Homes, Smart Factories, Robotics, and Computer Vision applications. 🎥 Explore the next generation of AI innovation. Watch AiM Future’s latest video now! #AiM Future #AI #Multimodal #SmartHome #SmartFactory #Robotics #ComputerVision #Innovation #Tech #Future Of AI