Applications of Robotics

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data.  2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro  -> RoboCasa produces N (varying visuals)  -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are creating tools to enable everyone in the ecosystem to scale up with us: - RoboCasa: our generative simulation framework (Yuke Zhu). It's fully open-source! Here you go: http://robocasa.ai - MimicGen: our generative action framework (Ajay Mandlekar). The code is open-source for robot arms, but we will have another version for humanoid and 5-finger hands: https://lnkd.in/gsRArQXy - We are building a state-of-the-art Apple Vision Pro -> humanoid robot "Avatar" stack. Xiaolong Wang group’s open-source libraries laid the foundation: https://lnkd.in/gUYye7yt - Watch Jensen's keynote yesterday. He cannot hide his excitement about Project GR00T and robot foundation models! https://lnkd.in/g3hZteCG Finally, GEAR lab is hiring! We want the best roboticists in the world to join us on this moon-landing mission to solve physical AGI: https://lnkd.in/gTancpNK

If you told farmers 10 years ago that robots powered by sunlight would replace chemicals, it would sound ridiculous. These solar-powered rovers use vision AI to identify and remove weeds at the plant level. No herbicides, no operators… not even lasers or anything crazy. Just going back to the original method of dealing with weeds, pulling them out, just done by machines now. The hard part is not building a robot that works in one field. It is building one that works in every field. Different crops, different soil, different weeds, different growth stages, different geographies. Farming has no standard environment. So Aigen trained their system using NVIDIA Cosmos foundation models and Isaac Sim pipelines to simulate millions of agricultural scenarios before deploying anything in the real world. On the ground, each rover runs inference using NVIDIA Jetson Orin to distinguish crops from weeds while moving. These systems need to become cheaper and more accessible than traditional methods. Once that happens, adoption becomes obvious, especially as demand for food keeps scaling globally. Farmers spend billions on herbicides. If robots can replace even part of that, you change both the cost structure and the environmental footprint at the same time. Follow Endrit Restelica for more.

I built a self-hosted AI architecture that runs without internet, no API cost, no cloud. AI works when network doesn't. This was the toughest project I’ve ever worked on and I did it to answer one question: Can we talk to AI when the internet is down and can we trust AI with sensitive data which cannot leave the building? Short answer: Yes. Meet Secure AI Lab. What it does: Works like ChatGPT, but lives on your computer and runs without internet Reads your own documents (protocols/policies) to answer with context. Automates tasks (save files, generate PDFs, log entries) locally. Runs fully offline after setup no cloud, no API keys, no telemetry. In the video, I switch Wi-Fi OFF and ask: “What medications are used for cardiac arrest??” OpenWebUI (local chatbot) answers from my local knowledgebase. n8n (local workflow) auto-creates a file on my disk with the summary. Every step happens on localhost. Nothing leaves the machine. ⚠️ Demo ≠ diagnosis. The medication shown is mock data; this is a clinical support example, not medical advice. Why this matters: Emergency Departments (ED) during downtime: keep triage guidance, protocol recall, and order prep running when EHR/internet is down. Hospitals, banks, factories: when privacy and reliability matter, local beats cloud. Cost control: one-time setup vs. indefinite per-token bills. How it works (simple flow) Inside the Lab: Local Brain – AI model (Ollama) generates answers on device. Your Documents – RAG reads your PDFs (protocols/policies) locally. Local Robot – n8n automations save files, generate PDFs, log to SQLite, print if needed. Not just Ollama offline. I built a complete offline system: chat UI + local RAG over my PDFs + automations that create PDFs/logs on disk, with Wi-Fi OFF and no egress. It’s a product, not just a model. I have added File-Watcher: when OpenWebUI saves a new answer, n8n auto-detects it and creates a PDF/log instantly, still with no internet. Stack at a glance OpenWebUI – local chat UI + RAG Ollama – runs the AI models on device n8n – no-code automations (write files, PDFs, logs) Docker – isolated, reproducible setup RAG – reads your docs; answers with citations SQLite/Files – local logs & artifacts (no cloud) This was my toughest build yet. I spent many weeks planning and stitching everything together to prove AI can run fully offline and still be useful in emergencies.

Humanoid robots are making robotics visible again. But the real challenge is not simply building a robot that can walk, lift, or manipulate objects. The real challenge is building the full stack around it. Any robot operating in the real world depends on far more than one impressive subsystem: • Sensors and compute • Embedded software • Perception and AI models • Planning and control • Safety systems • Cloud connectivity • Fleet operations • Cybersecurity • Data pipelines • Integration with customer infrastructure This is where robotics becomes difficult. A humanoid demo may show capability. But a production robot must show reliability, safety, maintainability, and economic value, day after day, in messy real-world environments. That requires deep integration across hardware, software, AI, cloud, safety, and operations. In my view, the real moat in robotics will not be one component. It will be integration complexity. The companies that scale robotics successfully will be those that can turn many complex subsystems into one reliable product experience. This also changes how robotics teams need to be built. The strongest robotics organizations will not look like pure hardware teams or pure AI teams. They will look like full-stack systems organizations, combining AI/ML, embedded software, controls, cloud platforms, safety engineering, cybersecurity, product integration, and field operations. Humanoids may be the visible symbol of the next robotics wave. But the real winner will be the team that can integrate the full stack well enough to make robots reliable, safe, and useful in the real world. Curious how others see this: Is the next robotics moat hardware, AI, or full-stack integration? #Robotics #AI #Humanoids #AutonomousSystems #IndustrialAI #SystemsEngineering

Across the world, sports facilities are turning to robotics to make pitch management faster, more accurate, and cost-effective. What do you think about this robot? 📊 Data-driven impact: + A traditional human crew takes 4–5 hours to mark a football field. Robotic systems like TinyMobileRobots can finish the same job in 25–30 minutes. + Accuracy is improved to within ±1 cm, reducing costly rework. + Clubs report up to 50–70% savings in labor costs over a season. 🌍 Real-world examples: + TinyMobileRobots: Used by 1,000+ clubs worldwide, including Premier League academies, to automate field marking. + Intelligent Marking (US/Europe): Pioneers of GPS-guided robots that deliver consistent results across multiple fields. + Fleet adoption in schools & universities: Many institutions now deploy robots to maintain multiple pitches efficiently. ✅ Efficiency – Complete fields in a fraction of the time. ✅ Accuracy – GPS and laser-guided technology ensure perfect lines. ✅ Sustainability – Reduced paint waste and optimized resource use. The result? Coaches and athletes can focus on performance, while automation redefines how we prepare for the game. The future of sports isn’t just about players—it’s about the smart technologies behind the scenes. Video credit: Kostas Panayotis Diakovasilis #Robotics #AI #Automation #SportsTech #Innovation #SmartSports

Through collaborations with Georgia Institute of Technology, Ground Control Robotics is developing a new locomotion paradigm for cluttered and confined environments. Wheels and long legs can't meet the demands of tough terrain, but these elongate mutilegged robots can. The robots have applications in agriculture, defense, search and rescue, and pest control. They're focusing first on the multi-billion dollar specialty agriculture market, where unstructured terrain makes traditional robots unusable.

Production changes everything. What worked in a demo starts breaking at scale. That’s where real AI systems are tested. Here are the concepts that actually matter 👇 - Prototype vs production A demo works in controlled conditions, while production systems deal with scale, failures, and messy edge cases. - Training vs inference Training happens occasionally to build the model, while inference runs continuously to serve real users. - Batch vs real-time inference Batch is cost-efficient for large workloads, while real-time is critical when user experience depends on instant responses. - Accuracy vs reliability Accuracy looks good on test data, while reliability shows consistent performance under real-world conditions. - Guardrails vs validation Guardrails prevent unsafe outputs, while validation ensures correctness. Both are needed for safe and dependable systems. - Offline vs online evaluation Offline testing uses past data, while online evaluation measures real user impact. One doesn’t guarantee the other. - Data drift vs model drift Data drift changes inputs, while model drift shows performance degradation. Detecting this early avoids silent failures. - Monitoring vs observability Monitoring tracks known issues, while observability helps you understand unknown failures and system behavior. - Model hosting vs model serving Hosting deploys the model, while serving handles scaling, routing, and real-time requests. This is where complexity grows. - RAG vs fine-tuning RAG brings in fresh external knowledge, while fine-tuning embeds knowledge into the model. One adapts, the other is fixed. - Latency vs throughput Latency is response speed, while throughput is volume. Systems often fail because latency becomes too high. - Prompting vs fine-tuning Prompting shapes behavior through instructions, while fine-tuning changes model weights. Many real systems rely more on prompting. Understanding these trade-offs is what makes AI systems actually work. Which of these has been the toughest in your production setup?

This is why so many demos never make it to production. Getting AI demos to work under controlled conditions is simple. Getting AI products to scale and support real-world operations or customers is completely different. An autonomous delivery drone with a 99.9% reliability isn’t as good as it sounds because it crashes every 1,000 trips. Trying to scale that up from a successful demo of 10 deliveries to doing 10 deliveries an hour for a week reveals the problem. In the digital paradigm, building the small circle solves most of the problems you’ll encounter building the biggest circle. In the data and AI paradigms, building the small circle teaches you very little about building the biggest one. Every data and AI minimum viable product scales on two axes: functionality and reliability. Meeting functional thresholds is always easier than meeting reliability requirements. The costs of small reliability improvements can be massive. The only way to learn how to build data and AI that scales is to build it for scale from the start. Building an AI demo doesn’t prove that the solution is viable or that scaling is feasible. It’s critical to build data and AI products iteratively, but we must change the way the business thinks about those iterations.

𝐕𝐢𝐛𝐞 𝐜𝐨𝐝𝐢𝐧𝐠 𝐢𝐬 𝐠𝐫𝐞𝐚𝐭 𝐟𝐨𝐫 𝐝𝐞𝐦𝐨𝐬 𝐚𝐧𝐝 𝐩𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞𝐬. It is dangerous if you ship it unchanged to production. We have all seen it: A "flawless" AI demo that wins the room, only to collapse the moment it touches real-world traffic. As an architect, I see teams optimize for optimism (the demo) when they should be optimizing for pessimism (the system). 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐫𝐢𝐬𝐞 𝐰𝐡𝐞𝐧 𝐀𝐈 𝐢𝐬 𝐬𝐜𝐚𝐥𝐞𝐝 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐃𝐞𝐦𝐨 𝐒𝐭𝐚𝐠𝐞 𝐭𝐨 𝐅𝐮𝐥𝐥 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 𝐃𝐞𝐦𝐨 𝐯𝐬 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 ENVIRONMENT: Demo → Clean, static, curated data. Production → Streaming, messy, incomplete, multi-source data. USE CASES: Demo → Prove accuracy. Impress stakeholders. Production → Drive business outcomes. Meet SLAs. Stay reliable. FFAILURE MODES: Demo → Hardcoded logic. No monitoring. Production → Drift, latency spikes, broken APIs, behavior changes. USER AND WORKFLOW ALIGNMENT: Demo → Data scientist, quick iteration. Production → End users, Ops, SREs, approvals, handoffs. PROMPT, MODEL AND DATA MANAGEMENT: Demo → Single model, one-time evaluation. Production → Versioning, canary releases, retraining, monitoring. In AI Production, it is not just about the accuracy of your models it is about scalability, resilience, and continuous monitoring to ensure consistent performance in dynamic real-world environments. THE PRINCIPLE Demo success = Accuracy Production success = Resilience + Monitoring 𝐖𝐇𝐀𝐓 𝐓𝐄𝐀𝐌𝐒 𝐆𝐄𝐓 𝐖𝐑𝐎𝐍𝐆 They optimize for demo metrics (accuracy, speed) instead of production requirements (reliability, drift detection, fallback strategies). They assume: "If it works in the demo, it will work in production." Reality: Demo conditions ≠ Production conditions. 𝐌𝐘 𝐑𝐄𝐂𝐎𝐌𝐌𝐄𝐍𝐃𝐀𝐓𝐈𝐎𝐍 I am not telling you to stop "vibe coding". It is the fastest way to innovate.  But in the enterprise, you must vibe with guardrails. Before calling it production-ready, ask: ✓ Can it handle messy live data? ✓ Does it meet SLAs under load? ✓ Does it detect drift? ✓ Is there versioning + rollback? ✓ Is monitoring in place? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #EnterpriseAI #AgenticAI

China is redefining logistics with a new kind of warehouse, one you can barely see📦 These “dark warehouses” operate 24/7 with no human workers on-site. Powered by AI, robotics, and advanced automation systems, they can process up to 200,000 parcels per day with incredible speed and precision. ▶️No lights. No breaks. No downtime. From autonomous sorting systems to robotic arms and intelligent routing algorithms, every movement is optimized for efficiency. The result? Faster delivery times, lower operational costs, and a supply chain that’s always on. But this shift raises important questions: ✅What does this mean for the future of warehouse jobs? ✅How will companies balance efficiency with workforce impact? ✅Are we ready for fully autonomous supply chains? One thing is clear: logistics is no longer just about moving goods, it’s about engineering intelligence at scale. The future of warehousing isn’t coming. It’s already here. #Automation #AI #Logistics #SupplyChain #Innovation #FutureOfWork #Robotics