Raju Singh’s Post

Gemini Robotics 1.5: Revolutionizing Robotics with DeepMind’s ER↔VLA AI Stack #GeminiRobotics #AIintegration #DeepMind #RoboticsInnovation #AutomationSolutions #AI #itinai #TechTrends #FutureOfWork https://lnkd.in/dRqZXfmd Gemini Robotics 1.5 by Google DeepMind marks a significant leap in the integration of artificial intelligence and robotics. Designed for business professionals, researchers, and developers, this innovative platform addresses common challenges faced in the fields of AI and automation. Understanding the target audience is crucial; these individuals often seek advanced solutions that enhance operational efficiency and drive innovation. Understanding the Challenges Many in the industry grapple with integrating advanced AI solutions into existing systems. High costs associated with retraining models for different tasks and ensuring the safety and reliability of autonomous systems are major pain points. The goal for these professionals is clear: they want scalable AI-driven solutions that not only boost productivity but also reduce operational risks. Overview of Gemini Robotics 1.5 The core of Gemini Robotics 1.5 lies in its sophisticated AI stack, which allows for advanced planning and reasoning across various robotic platforms without the need for extensive retraining. This is achieved through two groundbreaking models: Gemini Robotics-ER 1.5: This multimodal planner excels in high-level tasks like spatial understanding and progress estimation. It can also invoke external tools to enhance its planning capabilities. Gemini Robotics 1.5: Known as the vision-language-action (VLA) model, it executes motor commands based on the planner’s output, allowing for a structured approach to complex tasks. Architecture of the Stack The architecture of Gemini Robotics 1.5 separates reasoning from control, which significantly enhances reliability. The Gemini Robotics-ER 1.5 manages the planning and reasoning aspects, while the VLA is dedicated to executing commands. This modular approach not only improves interpretability but also aids in error recovery, addressing issues that previous systems faced with robust task planning. Motion Transfer and Cross-Embodiment Capability A key feature of Gemini Robotics 1.5 is its Motion Transfer (MT) capability. This allows the VLA to utilize a unified motion representation, enabling skills learned on one robot to be transferred to another—such as from ALOHA to bi-arm Franka—without the need for extensive retraining. This capability drastically reduces the data collection process and helps bridge the simulation-to-reality gap. Quantitative Improvements The advancements brought by Gemini Robotics 1.5 are not just theoretical; they have resulted in measurable enhancements: Improved instruction following and action generalization across multiple platforms. Successful zero-shot skill transfer, showcasing...

🌍 𝐓𝐡𝐞 𝐆𝐥𝐨𝐛𝐚𝐥 𝐇𝐮𝐦𝐚𝐧𝐨𝐢𝐝 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡: 𝐀𝐭𝐥𝐚𝐬, 𝐔𝐧𝐢𝐭𝐫𝐞𝐞 & 𝐌𝐨𝐫𝐞🤖 On August 26, 2025, Boston Dynamics unveiled a landmark upgrade for its flagship humanoid robot — Atlas. For those unfamiliar, Atlas has been the gold standard in humanoid robotics for over a decade, famous for its parkour, flips, and agility. But until now, much of that brilliance relied on carefully scripted behaviors and highly controlled programming. 𝐓𝐡𝐢𝐬 𝐥𝐚𝐭𝐞𝐬𝐭 𝐥𝐞𝐚𝐩 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠:  ✅ 𝘈𝘵𝘭𝘢𝘴 𝘪𝘴 𝘯𝘰𝘸 𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘣𝘺 𝘢 𝘓𝘢𝘳𝘨𝘦 𝘉𝘦𝘩𝘢𝘷𝘪𝘰𝘳 𝘔𝘰𝘥𝘦𝘭, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘪𝘵 𝘵𝘰 𝘤𝘰𝘯𝘵𝘳𝘰𝘭 𝘪𝘵𝘴 𝘦𝘯𝘵𝘪𝘳𝘦 𝘣𝘰𝘥𝘺 𝘢𝘴 𝘢 𝘶𝘯𝘪𝘧𝘪𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮. ✅ 𝘐𝘯𝘴𝘵𝘦𝘢𝘥 𝘰𝘧 𝘴𝘸𝘪𝘵𝘤𝘩𝘪𝘯𝘨 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘱𝘳𝘦-𝘱𝘳𝘰𝘨𝘳𝘢𝘮𝘮𝘦𝘥 𝘴𝘬𝘪𝘭𝘭𝘴, 𝘪𝘵 𝘤𝘢𝘯 𝘢𝘥𝘢𝘱𝘵 𝘧𝘭𝘶𝘪𝘥𝘭𝘺 𝘵𝘰 𝘯𝘦𝘸 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘵𝘪𝘮𝘦. ✅ 𝘛𝘩𝘪𝘴 𝘣𝘳𝘪𝘯𝘨𝘴 𝘪𝘵 𝘤𝘭𝘰𝘴𝘦𝘳 𝘵𝘩𝘢𝘯 𝘦𝘷𝘦𝘳 𝘵𝘰 𝘢 𝘨𝘦𝘯𝘦𝘳𝘢𝘭-𝘱𝘶𝘳𝘱𝘰𝘴𝘦 𝘩𝘶𝘮𝘢𝘯𝘰𝘪𝘥 — 𝘢 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘵𝘩𝘢𝘵 𝘭𝘦𝘢𝘳𝘯𝘴, 𝘳𝘦𝘢𝘤𝘵𝘴, 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘪𝘴𝘦𝘴. And Atlas isn’t alone. Globally, humanoid development is accelerating: . 𝘜𝘯𝘪𝘵𝘳𝘦𝘦 𝘵𝘦𝘢𝘴𝘦𝘥 𝘢 𝘧𝘶𝘭𝘭-𝘩𝘦𝘪𝘨𝘩𝘵 𝘩𝘶𝘮𝘢𝘯𝘰𝘪𝘥 𝘸𝘪𝘵𝘩 31 𝘫𝘰𝘪𝘯𝘵𝘴. . 𝘍𝘪𝘨𝘶𝘳𝘦 𝘶𝘯𝘷𝘦𝘪𝘭𝘦𝘥 𝘪𝘵𝘴 𝘏𝘦𝘭𝘪𝘹 𝘤𝘰𝘯𝘵𝘳𝘰𝘭𝘭𝘦𝘳, 𝘨𝘪𝘷𝘪𝘯𝘨 𝘳𝘰𝘣𝘰𝘵𝘴 𝘣𝘢𝘭𝘢𝘯𝘤𝘦 𝘴𝘶𝘳𝘱𝘢𝘴𝘴𝘪𝘯𝘨 𝘩𝘶𝘮𝘢𝘯𝘴. . 𝘚𝘰𝘶𝘵𝘩 𝘒𝘰𝘳𝘦𝘢’𝘴 𝘈𝘓𝘓𝘌𝘟 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥 𝘮𝘶𝘴𝘤𝘭𝘦-𝘭𝘪𝘬𝘦 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘧𝘰𝘳 𝘥𝘦𝘭𝘪𝘤𝘢𝘵𝘦 𝘵𝘢𝘴𝘬𝘴. . 𝘛𝘦𝘤𝘩𝘮𝘢𝘯 𝘳𝘦𝘷𝘦𝘢𝘭𝘦𝘥 𝘢𝘯 𝘕𝘝𝘐𝘋𝘐𝘈-𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘩𝘶𝘮𝘢𝘯𝘰𝘪𝘥 𝘵𝘢𝘳𝘨𝘦𝘵𝘪𝘯𝘨 𝘳𝘦𝘢𝘭-𝘸𝘰𝘳𝘭𝘥 𝘪𝘯𝘥𝘶𝘴𝘵𝘳𝘪𝘦𝘴. 💡 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫?  Humanoids are shifting from demos to deployable systems. The Atlas upgrade is significant because it: >Breaks the ceiling of scripted robotics, showing adaptability is possible. >Sets a benchmark for control and autonomy that competitors will chase. >Bridges the lab-to-field gap — imagine robots reacting to rubble in disaster zones, walking steadily through crowded hospitals, or adapting instantly on factory floors. >Inspires the robotics community to rethink how we design learning systems — moving from narrow, task-specific robots to general, versatile assistants. We’re not just seeing robots move better. We’re seeing the birth of robots that think about how to move. That’s a turning point. 🔗 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐫𝐞𝐚𝐝 𝐦𝐨𝐫𝐞?  Check out Boston Dynamics’ Atlas project page:-https://lnkd.in/enKgd5t5 for official updates and insights. ❓ The big question: If humanoids can now learn and adapt almost like humans, how far are we from trusting them as collaborators in everyday life? #Atlas #BostonDynamics #Unitree #Robotics #Humanoids #AI #Innovation #TechBreakthrough

🚀 Big Moves in AI Robotics! 🤖 Google DeepMind just unveiled Gemini Robotics-ER 1.5 and Gemini Robotics 1.5—two groundbreaking AI models designed to bridge the gap between the virtual and physical worlds! 🌐➡️🏭 These new models aren’t just about AI thinking—they’re about AI acting, enabling robots to perform complex, multi-step tasks with remarkable precision and adaptability. With cutting-edge features like vision-language-action frameworks and embodied reasoning, these robots can now navigate real-world environments safely and efficiently. This is a major leap toward smarter automation, with potential to transform industries like healthcare, manufacturing, and logistics. 🤯 🔗 Curious how these advancements will shape the future of work and automation? Check out the full details in the link below! https://lnkd.in/gJ469Z9w #AI #Robotics #Automation #DeepMind #ArtificialIntelligence #Innovation #FutureOfWork

Google DeepMind’s unveiling of Gemini Robotics-ER 1.5 and Gemini Robotics 1.5 marks a pivotal moment in AI's evolution from the digital realm into the physical world. With these models, robots are no longer just thinking—they’re acting. What excites me most is the integration of vision-language-action frameworks and embodied reasoning. These features allow robots to perform complex, multi-step tasks in dynamic, real-world environments with greater adaptability and safety. Imagine AI systems handling everything from manufacturing to healthcare—autonomously navigating and interacting with the physical world in ways we've only dreamed about. As AI begins to bridge the gap between virtual and physical realms, it raises profound questions on automation’s future and its impact on industries like healthcare, logistics, and manufacturing. This feels like a major step toward smarter, safer robots that can truly enhance human capabilities in everyday tasks.

Robots That Think Before They Move—The Future Is Vision-Language-Action 👀🧠🚀 We’re seeing a big leap—robots that don’t just execute instructions, but reason about their environment and decide how to act. 🔍 What’s New: * Google DeepMind introduced Gemini Robotics-ER 1.5, a model that reasons in physical space, plans multi-step tasks, and even calls tools like web search. Then it hands off execution to Gemini Robotics 1.5, the vision-language-action component. * This duo allows robots to “think before acting”—they can perceive changes, interpret context, split tasks, and choose the right path forward. * These systems also support cross-embodiment learning: skills learned on one robot type transfer to different platforms (e.g. from robotic arms to humanoids). ✅ How You Can Use It: 1️⃣ Prototype a vision-to-action pipeline: feed an image + instruction → planning model → execution model. 2️⃣ Leverage tool calling (e.g. web lookup) to supplement missing context or information. 3️⃣ Test in simulation first, then on physical platforms with safety checks. Pro Tip: Don’t just teach robots “how”—teach them why. Reasoning models + action models = smarter, safer, more adaptable robotics. 📚 🔗 https://lnkd.in/geyjR2VV 🔗 https://lnkd.in/gUHTcEET 🔗 https://lnkd.in/g-GZKXG6 👇 What use case would you try first with a reasoning robot (e.g. warehouse picking, home chores, inspection)? #AI #Robotics #VLA #VisionLanguageAction #GeminiRobotics #AgenticAI #Innovation

🚨 In case you missed it, Google DeepMind recently unveiled Gemini Robotics 1.5 — models that let robots not only plan multi-step tasks but also search the web in real time to solve problems. https://lnkd.in/gvv3PzQu This means a robot could separate recycling based on local rules, pack a suitcase for London weather, or even transfer learned skills across completely different machines. At D!srupt AI, we see this as a turning point: AI moving from digital assistants to embodied intelligence, bridging data, context, and the physical world. The implications stretch far beyond robotics — into workforce transformation, civic systems, and the future of human-machine collaboration. Our mission is to pursue the impossible: not just building smarter machines, but reshaping economies and communities through applied AI. Breakthroughs like this reinforce why we’re investing in trusted data curation, community labs, and real-world pilots here in New Hampshire... so these capabilities can serve everyone. 👉 What excites (or concerns) you most about robots tapping the web to act in the physical world? #AI #Robotics #AppliedAI #DisruptAI

🚀 𝐑𝐨𝐛𝐨𝐭𝐬 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐭𝐡𝐢𝐧𝐤, 𝐩𝐥𝐚𝐧, 𝐚𝐧𝐝 𝐟𝐞𝐭𝐜𝐡 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐰𝐞𝐛 Google DeepMind just announced Gemini Robotics 1.5 (and its partner model, Gemini Robotics-ER 1.5) — a leap toward agentic robots that can perceive, reason, use web tools, and act in the physical world. Read more on The Verge: https://lnkd.in/dDuGzGvw This isn’t just “robots following commands.” It’s robots with agency - able to break down goals, seek the info they lack, and then act. 💡 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: ● AI is moving beyond text/chat into the physical realm. ● Agentic AI isn’t a theory anymore — the boundary between digital assistants and physical collaborators is shrinking. ● The architecture (reasoning + execution + tool access) could become a blueprint for next-gen robotics. For me, this raises both excitement and questions: ● How will industries (from logistics to healthcare) rework workflows around autonomous agents? ● What safety and interpretability layers are necessary before deployment at scale? ● Are we investing enough in human-AI collaboration frameworks (not just the tech)? 👉 I’d love to hear your take: Are we ready for AI that doesn’t just answer, but acts? #AI #Robotics #AgenticAI #FutureOfWork #DeepMind

Too stupid for life: People hoping for household robots soon will be disappointed. A robot folds laundry, sorts trash, and responds to voice commands - Google DeepMind demonstrates what AI-controlled machines can do today. But a new academic paper calls for sobriety: Truly "thinking" robots are still a distant prospect. Google DeepMind vs. Reality: Are Household Robots Still a Utopia? Humanoid robots have been considered the goal of modern engineering for decades. Systems such as Tesla's Optimus, Boston Dynamics' Atlas, and Apptronik's Apollo are designed to learn to perform complex tasks independently. The latest boost is provided by AI – particularly multimodal language models designed to see, understand, and act. Google DeepMind is considered one of the most important drivers of this development. With the new models Gemini Robotics 1.5 and Gemini Robotics-ER 1.5, the company combines visual sensing, language understanding, and motor skills. In demonstration videos, the robot "Apollo" appears to understand commands – such as folding laundry or placing objects in bags. Technically, AI translates image data and speech into movement commands, plans paths, and prioritizes tasks. This is precisely where a new analysis in the journal "Nature Machine Intelligence" comes in. The team, consisting of Aude Billard (EPFL) and Ravinder Dahiya from Northeastern University, calls in their roadmap for AI models to be more closely linked to physical perception. The current generation can recognize scenes, but cannot process tactile, thermal, or chemical impressions – key prerequisites for safe action in unpredictable environments, according to the researchers. The data is missing Vision-language-action models like Gemini are based on massive amounts of data. They recognize patterns, but not meaning. Dahiya is working on electronic skin, among other things, whose sensor points can detect pressure, temperature, and texture. The major problem for an AI breakthrough in understanding such sensor impressions: Unlike with visual training data, there is no large "tactile dataset" to extend machine learning to this area. A major gap in the robots' "worldview" that can't be easily closed. But true autonomy, the researchers conclude, only emerges when robots combine multiple sensory channels: sight, hearing, touch, and smell. Only then can machines flexibly deal with uncertainty—from fragile glass to a hot pan. Therefore, the team concludes: Until then, the humanoid household helper remains a fascinating experiment on the long road to embodied intelligence.

🚀 Can robots really “understand” the world? Gemini Robotics‑ER 1.5 might be a step closer. After reading this in-depth article from Il Sole 24 Ore: 🔗 https://lnkd.in/dR5SA9j8 …I started diving deeper into the technical foundations of Google DeepMind's Gemini Robotics‑ER 1.5 — and what it means for the future of embodied AI. 🔍 What stood out to me: A) The model links natural language, visual input, and spatial understanding to execute robotic tasks in the real world. B) It’s trained in simulation, then refined in physical environments — a common strategy to close the sim-to-real gap. 📄 https://lnkd.in/dhStzGzZ C) It shows cross-embodiment generalization, transferring skills across different robot types. 📄 Technical model card: https://lnkd.in/dVjpWz9a D) The architecture builds on recent research integrating perception, reasoning, and action: 📄 Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation https://lnkd.in/dc-AVdFq E) Planning can be supported by external tool use (e.g. web search), enabling decisions grounded in real-world context. 📄 RobotxR1: Enabling Embodied Robotic Intelligence on LLMs through Closed-Loop Reinforcement Learning https://lnkd.in/djtGSp7B ⚠️ But several open challenges remain: 1) The sim-to-real gap is still a fundamental issue in robotics 📄 A Survey: Learning Embodied Intelligence from Physical Simulators and World Models https://lnkd.in/dN9uyntr 2) Safety and alignment are critical when real-world physical actions are involved 📄 Towards ASIMOV Benchmarks: Evaluating Safety, Alignment, and Value Alignment in Embodied Agents https://lnkd.in/dNMY286q 3) Generalization across unseen environments, occlusions, or failure modes still lacks strong guarantees 4) Hardware (sensors, actuators) can limit the practical performance of intelligent agents 💬 From my perspective, Gemini‑ER 1.5 is an exciting direction. It's a real-world testbed for the convergence of LLMs, robotics, and multimodal perception, but we’re still early in the journey toward deployable, general-purpose embodied intelligence. What do you think, how far are we from reliable, useful robots that can act safely and adaptively in dynamic environments?

AI can now see, hear, and even feel. Embodied AI is stepping out of the digital world. Google DeepMind’s Gemini Robotics 1.5 is bringing agentic AI into the physical world, teaching robots to perceive and act like humans. Are we ready for intelligence that senses, reasons, and moves on its own? #EmbodiedAI #AgenticAI #ResponsibleAI #AIandEthics #FutureOfAI https://lnkd.in/gbvTcv5J