Applications of Robotics

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic. This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration. The Workflow at a Glance 1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools). 2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components. 3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.     This feedback loop mirrors principles from: • The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act) • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI • 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires: • A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making • Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness • Tool-use and reasoning across multiple steps • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion • The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback    This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break. Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking. Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯. Curious to hear how you're modeling cognition in your systems.

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

computer vision + robotics 🔥 🔥 🔥 Over the last few days, I trained my Reachy Mini robot to track and follow a human face. I fine tuned RF-DETR Nano on a custom face detection dataset. The system maps pixel coordinates of a detected face to yaw and pitch commands for head control. I plan to release the code soon. The main issue is inertia. The robot head has significant mass. At higher angular velocities, inertia causes overshoot. During the next control step, the controller overcompensates. This behavior leads to oscillations. ⮑  RF-DETR: https://lnkd.in/dVQRpvWU #computervision #opensource #objectdetection #robotics

As we return to work after the holiday break with energy and renewed focus, I want to challenge you to think deeply in the coming year about how you can use your unique skills to solve problems with the greatest impact. Start with those closest to you, the people in your community. Consider loneliness. It has become a public health crisis, affecting one in six people worldwide with numbers increasing significantly with age. The technology designed to connect us, such as social media, smarter phones, has made many feel more isolated. But if technology is a cause, it can also be a solution. Companion robots have shown real promise in addressing this crisis. In Canada, long-term care facilities and hospitals have adopted robots like Pepper, Paro, and Lovot to support mental health and wellbeing. A study of Paro found 95% of dementia patients showed measurable improvements in mood, reduced agitation, and better sleep. We are still early days, but what’s clear is that the solution requires us to think deeply about how to use technology with purpose. Not to think in black and white. As technologists, it is our duty to solve hard human problems with technology, not create them. For more, read my full tech predictions for 2026 and beyond here https://lnkd.in/eHjPnxYB Now Go Build

𝗧𝗵𝗲 𝗥𝗢𝗕𝗢𝗧 𝗥𝗘𝗩𝗢𝗟𝗨𝗧𝗜𝗢𝗡 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗿𝗲! 🖤 12 𝘳𝘦𝘢𝘭-𝘸𝘰𝘳𝘭𝘥 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦𝘴 𝘵𝘩𝘢𝘵 𝘤𝘢𝘯 𝘮𝘢𝘬𝘦 𝘢𝘯 𝘪𝘮𝘱𝘢𝘤𝘵. They’re entering factories, hospitals, warehouses, hotels, construction sites and even our homes. Over the weekend, I looked at where robots are already creating measurable business value already today. Here are 12 real-world robot use cases: → 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗣𝗮𝗿𝗸𝗶𝗻𝗴 𝗥𝗼𝗯𝗼𝘁𝘀 Park cars automatically, maximize parking capacity and eliminate the frustration of finding a parking spot. → 𝗣𝘂𝗯𝗹𝗶𝗰 𝗥𝗲𝘀𝘁𝗿𝗼𝗼𝗺 𝗖𝗹𝗲𝗮𝗻 𝗨𝗽 𝗥𝗼𝗯𝗼𝘁𝘀 Clean and sanitize public restrooms around the clock, improving hygiene while taking over repetitive cleaning tasks. → 𝗛𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗥𝗼𝗯𝗼𝘁 𝗦𝘂𝗿𝗴𝗲𝗼𝗻𝘀 Assist surgeons with highly precise procedures and have the potential to improve consistency while reducing fatigue during long operations. → 𝗚𝗮𝘀 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝗡𝗶𝗴𝗵𝘁 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗥𝗼𝗯𝗼𝘁𝘀 Autonomously refuel or recharge vehicles during nighttime hours, allowing stations to operate with minimal staff. → 𝗚𝗿𝗮𝗶𝗻 𝗦𝗶𝗹𝗼 𝗜𝗻𝘀𝗽𝗲𝗰𝘁𝗶𝗼𝗻 𝗥𝗼𝗯𝗼𝘁𝘀 Inspect grain silos where dust explosions and dangerous working conditions pose serious risks to human workers. → 𝗗𝗲𝘅𝘁𝗲𝗿𝗼𝘂𝘀 𝗥𝗼𝗯𝗼𝘁 𝗛𝗮𝗻𝗱𝘀 𝗳𝗼𝗿 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 Perform delicate assembly tasks with remarkable speed, precision and consistency. → 𝗡𝗘𝗢 𝗚𝗮𝗺𝗺𝗮 𝗳𝗼𝗿 𝗛𝗼𝘂𝘀𝗲𝗵𝗼𝗹𝗱𝘀 Fold laundry, unload the dishwasher and support people with everyday household chores. → 𝗙𝗶𝗴𝘂𝗿𝗲 𝟬𝟯 𝗳𝗼𝗿 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 Supply production lines, transport materials and take over repetitive factory work. → 𝗔𝗺𝗮𝘇𝗼𝗻 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗥𝗼𝗯𝗼𝘁𝘀 Move inventory autonomously through fulfillment centers, making logistics faster and more efficient. → 𝗛𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗪𝗲𝗹𝗱𝗶𝗻𝗴 𝗥𝗼𝗯𝗼𝘁𝘀 Handle hazardous welding tasks, reducing worker exposure to heat, fumes and dangerous environments. → 𝗧𝗶𝗹𝗲-𝗟𝗮𝘆𝗶𝗻𝗴 𝗥𝗼𝗯𝗼𝘁𝘀 Lay floor tiles with consistent quality while significantly increasing construction productivity. → 𝗛𝗶𝗴𝗵-𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗖𝗮𝗯𝗹𝗲 𝗥𝗲𝗽𝗮𝗶𝗿 𝗥𝗼𝗯𝗼𝘁𝘀 Maintain and repair live power lines without putting human workers in life-threatening situations. After looking at all these examples, I noticed something interesting. Almost every robotics application solves one (or more) of just three challenges: ☑️ Efficiency – doing work faster, cheaper and more consistently. ☑️ Labor shortages – filling roles where there simply aren’t enough people. ☑️ Dangerous work – protecting people by taking over hazardous tasks. If you’re exploring robotics for your business, don’t start by asking: “𝘞𝘩𝘪𝘤𝘩 𝘳𝘰𝘣𝘰𝘵 𝘴𝘩𝘰𝘶𝘭𝘥 𝘸𝘦 𝘣𝘶𝘺?” Start by asking: “𝘞𝘩𝘪𝘤𝘩 𝘰𝘧 𝘵𝘩𝘦𝘴𝘦 𝘵𝘩𝘳𝘦𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮𝘴 𝘢𝘳𝘦 𝘸𝘦 𝘵𝘳𝘺𝘪𝘯𝘨 𝘵𝘰 𝘴𝘰𝘭𝘷𝘦?” 𝗟𝗲𝘁’𝘀 𝘂𝘀𝗲 𝗿𝗼𝗯𝗼𝘁𝘀 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿 𝘄𝗼𝗿𝗹𝗱. 🤍🦾 Which robot use case do YOU think will have the biggest impact over the next five years?

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

The Humanoid Robot Revolution: Timelines, Leadership & Business Impact (Mid-2026 Update) Humanoid robotics has shifted from prototypes to real-world deployment faster than most expected. This is one of the most transformative technological accelerations of our time. Current Leaders Delivering Results: - Figure AI robots are already working in BMW plants on complex tasks. - Tesla is scaling Optimus production, targeting broader availability in late 2026–2027. - Chinese companies (Unitree, Agibot, UBTECH) have shipped thousands of units, leading in volume, cost, and early profitability. - 1X, Agility Robotics (Digit), Boston Dynamics (Atlas) and others are advancing pilots into enterprise and early consumer use. What to Expect: - 2026–2027: Industrial deployments scale across manufacturing, logistics, and warehousing. Early home/service models emerge. Prices drop rapidly. - 2028–2030: Tens to hundreds of thousands deployed globally. Humanoids become reliable 24/7 partners for repetitive and hazardous work. - 2030+: Mass adoption with millions of units, creating a new layer of physical AI that augments human capabilities at scale. Driven by AI breakthroughs, real-world data, and hardware iteration, years of projected progress have been compressed into months. How This Changes Everything Humanoids address labor shortages, aging populations, and human physical limits. They enable 24/7 operations, supply chain resilience, manufacturing reshoring, and lower costs, driving higher productivity and economic abundance. Who Will Lead? A global race with multiple winners: - Tesla: Scale, vertical integration, and AI-data flywheel. - U.S. innovators (e.g. Figure AI): Sophisticated enterprise AI systems. - Chinese manufacturers: Speed, cost leadership, and volume. - Specialists (Boston Dynamics, Agility, 1X): Agility and targeted applications. Impact on Business & Jobs Businesses: Early adopters gain major advantages in cost, output, flexibility, and resilience. Robot-as-a-Service models are emerging. Late adopters will struggle to compete. Jobs: Not replacement, but transformation. Routine, dangerous, and physically demanding roles will be automated, while new opportunities grow in robot maintenance, programming, integration, and entirely new fields. Technology has always created more value and jobs than it displaces. This is a story of amplification: human ingenuity + intelligent machines = safer work, better services, and more space for creativity, strategy, and meaningful contributions. The acceleration is here. The window to prepare is now. What timeline do you see for humanoid adoption in your industry? How is your organization getting ready?

First fully open Action Reasoning Model (ARM); can ‘think’ in 3D & turn your instructions into real-world actions: [📍 Bookmark for later] A model that reasons in space, time, and motion. It breaks down your command into three steps: ✅ Grounds the scene with depth-aware perception tokens ✅ Plans the motion through visual reasoning traces ✅ Executes low-level commands for real hardware Think of it as chain-of-thought for physical action. Give it an instruction like “Pick up the trash” and MolmoAct will: 1. Understand the environment through depth perception 2. Visually plan the sequence of moves 3. Carry them out… while letting you see the plan overlaid on camera frames before anything moves It’s steerable in real time: draw a path, change the prompt, and the trajectory updates instantly. AAAANNNDDD: It’s completely open: checkpoints, code, and evaluation scripts are ALL PUBLIC! Resources Models: https://lnkd.in/dcMVV29k Data: https://lnkd.in/dUwszSvd 📍Blog: https://lnkd.in/diNJFXEi MolmoAct runs across different robot types (from gripper arms to humanoids) and adapts quickly to new tasks. It outperforms models from major labs like NVIDIA, Google, and Microsoft on benchmark tests for generalization and real-world success rates. For anyone building robotics systems or studying AI-driven action models, this is worth exploring… and worth sharing! ♻️

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?