Strategies for Large-Scale Robotics Integration

Controlling 10,000 drones with a single computer is a complex task that involves multiple technologies working together to manage communication, coordination, and flight operations effectively. Here are some key technologies that can be used to achieve this: Swarm Intelligence: Algorithms inspired by social insects like bees or ants can help coordinate and manage large numbers of drones to work together as a cohesive unit. Distributed Computing: Leveraging distributed computing allows processing tasks to be shared among drones, reducing the load on a single computer. Cloud Computing: Using cloud infrastructure can provide the computational power and storage needed to process large amounts of data and commands for the drones. Real-time Communication Protocols: Efficient protocols, such as MQTT (Message Queuing Telemetry Transport) or DDS (Data Distribution Service), support low-latency communication between the control system and drones. Mesh Networking: This network topology enables drones to communicate with each other directly, forwarding data to extend range and reliability. AI and Machine Learning: AI algorithms can optimize flight paths and decision-making, enhancing the ability to manage large drone swarms. GPS and GNSS: These systems provide precise location data necessary for coordinating drone movements and ensuring they follow the correct paths. 5G Connectivity: High-speed, low-latency networks like 5G can significantly improve communication between drones and the control computer. Edge Computing: Processing data on the drones themselves can reduce latency and bandwidth by only sending essential data back to the main control system. Autonomous Navigation Systems: Technologies such as SLAM (Simultaneous Localization and Mapping) allow drones to navigate independently, reducing the control load. Simulation and Digital Twin Technology: These tools help model and plan drone missions effectively, optimizing performance and reducing risks before deployment. Integrating these technologies can enable effective management of large drone fleets, allowing for coordinated operations across various applications, from logistics to surveillance.

From Pilot Purgatory to Scaled ROI: A Practical Playbook for Physical AI Physical AI is at an inflection point. The technology is advancing rapidly—robotics, autonomy, and intelligent systems are no longer the constraint. But overcoming pilot purgatory takes more than technology. It’s an operations, integration, and trust problem. Based on what I’ve seen leading Physical AI deployments at scale, here’s a practical playbook that works: ⸻ 1. Start with high-value use cases Focus where the operational pain is real—and economically meaningful: → High cost → High turnover → High injury ⸻ 2. Co-design with operators, not just R&D Adoption is designed—not assumed. Work backwards from real workflows, constraints, and KPIs. The fastest way to fail is to build in isolation. ⸻ 3. Run real-world pilots early A demo is not a deployment. Pilots designed for scale should look like production: → Real environments → Real constraints → Real performance metrics ⸻ 4. Treat integration as the real work This is where most efforts break down. Integration is not a phase—it is the work. Physical AI touches: → Processes → Systems → Data → Exception handling → Human-machine interaction ⸻ 5. Scale with trust At scale, three things matter. If any one is weak, adoption stalls. If all three are strong, scale accelerates: → Safety → Reliability → Economics ⸻ The bottom line: Physical AI won’t be won by the best demo. It will be won by those who can operationalize, integrate, and scale with trust. ⸻ I’d love to hear—where is your organization today: piloting, or scaling? #PhysicalAI #Robotics #Automation #SupplyChain #AITransformation #Operations #Leadership #Innovation

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

AI agents and physical AI are shifting industrial automation from equipment supply to autonomous, self-optimizing systems. The most mature vendors are moving from pilots to production, with robots navigating complex environments and digital twins optimizing the value chain. This CB Insights brief gives a good view of where the top 20 industrial automation companies stand on AI maturity. Three key trends. 1. Leaders like Siemens Industry and ABB are linking AI systems across design, logistics, manufacturing, and maintenance creating compounding benefits. 2. Optimization dominates near-term priorities, while digital twins are emerging as the backbone for connecting hardware and software. 3. Partnerships with tech companies like Microsoft, Google, and Nvidia are essential, but they create new dependencies that must be managed. Siemens at the top of the ranking, combining copilots, edge platforms, and digital twins. Its work with Microsoft and Nvidia expands capabilities but increases reliance on external tech. Honeywell takes a more focused approach, embedding AI into devices and workflows. Its Qualcomm partnership highlights product-level integration over broad system building. ABB advances through its OmniCore platform and acquisitions such as Sevensense and SensorFact, blending robotics, software, and energy management. Schneider Electric pushes AI in energy management, using digital twins and partnerships with Nvidia, Microsoft, and Itron to extend from factory optimization into grid intelligence. The path forward in industrial AI is moving beyond pilots or isolated tools. It will depend on how well vendors embed AI into their platforms, link technologies across domains, and balance the benefits of external partners with the need for strategic independence. Those that will get it right will turn AI from experimentation into durable advantage. Just as critical is how their customers adopt these technologies. Industrial firms must shift from isolated use cases to embedding AI in design, production, energy, and logistics. Success requires not only advanced tools, but also the data, skills, and processes to make AI scale in complex operations.

Happy to share our latest paper, "Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent". This work was led by Rob R. in collaboration with Marcel Kaufmann, Jonathan Becktor, Sangwoo Moon, Kalind Carpenter, Kai Pak, Amanda Towler, Rohan Thakker and myself. Please find the #OpenSource code, paper, and video demonstration linked below. Operating autonomous robots in the field is often challenging, especially at scale and without the proper support of Subject Matter Experts (SMEs). Traditionally, robotic operations require a team of specialists to monitor diagnostics and troubleshoot specific modules. This dependency can become a bottleneck when an SME is unavailable, making it difficult for operators to not only understand the system's functional state but to leverage its full capability set. The challenge grows when scaling to 1-to-N operator-to-robot interactions, particularly with a heterogeneous robot fleet (e.g., walking, roving, flying robots). To address this, we present the ROSA framework, which can leverage state-of-the-art Vision Language Models (VLMs), both on-device and online, to present the autonomy framework's capabilities to operators in an intuitive and accessible way. By enabling a natural language interface, ROSA helps bridge the gap for operators who are not roboticists, such as geologists or first responders, to effectively interact with robots in real-world missions. In our video, we demonstrate ROSA using the NeBula Autonomy framework developed at NASA Jet Propulsion Laboratory to operate in JPL's #MarsYard. Our paper also showcases ROSA's integration with JPL's EELS (Exobiology Extant Life Surveyor) robot and the NVIDIA Carter robot in the IsaacSim environment (stay tuned for ROSA IssacSim extension updates!). These examples highlight ROSA's ability to facilitate interactions across diverse robotic platforms and autonomy frameworks. Paper: https://lnkd.in/g4PRjF4V Github: https://lnkd.in/gwWXmmjR Video: https://lnkd.in/gxKcum27 #Robotics #Autonomy #AI #ROS #FieldRobotics #RobotOperations #NaturalLanguageProcessing #LLM #VLM

Every robotics evaluation I've seen does the same thing. Throughput comparisons. Safety reviews. Pricing. References. Site visits. All strong. Then integration shows up as a single bullet on slide 27: "Standard REST API. Well-documented." Everyone nods. The meeting moves on. By the time IT gets fully pulled in, a lot of concrete has already dried. There's a preferred vendor. A business case with dates. Savings baked into a financial plan. And the conversation shifts from "what do we really need?" to "how do we make this fit inside what's already promised?" I've watched this play out enough times to know where it leads. Robots that technically work but don't see inventory the way operators do. IT juggling day-to-day support plus a complex new integration nobody scoped properly. A business case that never accounted for the WMS vendor SOW, the middleware work, or the 3-6 months of stabilization. A polished API PDF does not mean your WMS and IT landscape are ready. The question I care about is simpler: "Can we integrate with OUR systems, by THIS date, with THIS team?" If that answer is fuzzy, the project is carrying more risk than the deck admits. One client changed a single policy that shifted everything: no vendor advances to final round without a live 2-hour integration workshop with the WMS team and lead integration engineer. On site. No slides. Vendors who weren't serious self-selected out. The short list got built around who could live with the existing WMS reality, not who had the shiniest robot. I broke this down deeper — including a 3-bucket readiness check and a full integration playbook — in the Warehouse Robotics Insights newsletter. #warehouseautomation #warehouserobotics #supplychain #wms

Yesterday I shared Job #1 : fist ever the AI Sovereignty Architect and it struck a nerve with a lot of people . Today is the AI job hiding in plain sight on factory floors worldwide. Here’s what just happened: Tesla began mass production of Optimus Gen 3 at Fremont while discontinuing Model S and X to make room for robots. Over 1,000 units are now deployed autonomously sorting parts and running quality checks. Boston Dynamics unveiled production-ready Atlas at CES 2026. Hyundai immediately committed to tens of thousands of units, backed by a $26B investment including a factory producing 30,000 bots per year. Figure AI’s BotQ facility is tooled for 12,000 humanoids annually, scaling to 100,000. Goldman Sachs projects up to 100,000 humanoid shipments in 2026. This isn’t coming. It’s here. But nobody is asking the critical question: Who manages the integration of 500 robots into a workforce of 2,000 humans? Not the engineer who built them. Not the factory manager. Not IT. Not HR. So i am calling this the Physical AI Fleet Integration Manager. This role sits at the intersection of operations, robotics, workforce psychology, and safety. On any given day they might: → Design hybrid human-robot workflows across production lines → Manage the fleet such as battery rotations, failure tracking, field repairs across hundreds of units → Lead the human transition such as retraining, managing resistance, building trust between people and machines → Own safety compliance under ISO 10218 and ISO/TS 15066 as AI-driven autonomy rewrites the rulebook → Build the feedback loop and capturing operational data and feeding it back into training systems McKinsey analyzed 800 occupations and found current tech could automate 57% of US work hours. But the future isn’t replacement. they call “skill partnerships” between people and robots. Hyundai’s CES announcement explicitly described “human-centered automation.” The International Federation of Robotics’ 2026 report states: “Close cooperation with employees in implementing robots plays a crucial role to ensure acceptance.” Someone has to orchestrate that acceptance. That’s this job. How to prepare for this job: Foundation in operations management or industrial engineering. Layer on robotics safety standards and the NVIDIA Isaac/Omniverse platform for digital twin simulation. Add what separates you from everyone: organizational change management and human factors engineering. The hardest part of deploying 500 robots isn’t the technology. It’s convincing 2,000 humans their lives get better, not worse. In 3 years, every Fortune 500 manufacturer will need one. Tomorrow will be Job #3. This one involves an industry that will surprise you.

JLG Industries’ acquisition of Canvas formalizes a six year lineage of co-development, dual platform integration, and jobsite validation. The agreement combines reliable mechanical platforms with task specific robotics, directly addressing manpower and risk constraints by delegating tasks to equipment already accepted in field conditions. The advantage centers automating interior tasks to reduce dependence on scarce skilled labor while lowering ergonomic risk. Architecturally, this favors resilient designs where autonomy operates locally at the edge, maintaining productivity even when connectivity, staffing, or site conditions fluctuate. The competitive implication is structural rather than incremental. Firms that can deliver task capable platforms will compress deployment cycles and reduce rework across projects. For operators and system designers, how might platform centric robotics reshape competitive advantage in industries defined by thin margins and execution risk? #ConstructionRobotics #IndustrialTech #EdgeComputing #Autonomy