Strategies to Advance Next-Generation Robotics

Reindustrializing #Europe in the age of AI 🤖”—our latest report outlines what it will take: Amid intensifying global competition in AI and #Robotics, Europe faces a defining moment: reindustrialize or risk falling irreversibly behind. Robotics can help restore industrial sovereignty, address demographic headwinds, and boost productivity. We propose a 5-point strategic roadmap to reposition Europe as a credible competitor alongside the US and China: 1️⃣ A European Robotics Roadmap – Focus on building champions in high-impact, under-robotized sectors: logistics, hospitality, agrifood, healthcare, aerospace, and defense. Prioritize strategic autonomy, not chasing lost ground in humanoids or autonomous vehicles. 2️⃣ Capital Access for Robotics Startups – Address the 7x VC funding gap with the US by scaling Europe’s venture capital market and reinforcing complementary funding streams. 3️⃣ Bridging Innovation and Market – Tackle fragmentation through innovation clusters, regional champions, and greater public-private investment coordination. We recommend increasing the 2028–2034 EU budget by at least 5% with a dedicated robotics allocation. 4️⃣ Upskilling the Workforce – Tackle skill shortages across factory floors and engineering teams. From frontline operators to system integrators, we need a unified "Robot Skills Framework" and modern vocational training. 5️⃣ Smart Regulation – Align AI and robotics regulation to promote innovation. Use regulatory sandboxes, harmonized safety standards, and dynamic, risk-based approaches to support adoption—especially among SMEs. 📘 Download the full report: https://lnkd.in/evxEPDgn #Robotics #AI #IndustrialPolicy #Reindustrialization #Innovation #VentureCapital #FutureOfWork #TechSovereignty #Automation #Manufacturing #Ludonomics #AllianzTrade #Allianz

The world of Robotics just changed overnight. And it’s been in the works for years. I still remember how excited we were when NVIDIA’s Jensen Huang gave a unique shout-out to Gideon at #GTC21 (check out the video below), hinting at what would eventually happen when the worlds of AI and Robotics collide. Jensen back then: "The signs are clear: accelerated computing doing AI at data center scale will give a giant boost in simulation performance." Jensen today: "Everything that moves in the future will be robotic." NVIDIA Robotics just announced a series of robotics breakthroughs at NVIDIA GTC, with a clear aim of democratizing the building of AI Robots with game-changing foundational components and tools: • Isaac Manipulator, a collection of state-of-the-art motion generation and modular AI capabilities for robotic arms, • Isaac Perceptor, Visual AI for Autonomous Mobile Robot (watch out if you’re building smart AMRs!), • GR00T, a general-purpose foundation model for humanoid robot learning, • a new Jetson Thor-based computer for humanoid robots, built on the NVIDIA Thor SoC, • Isaac Lab for robot learning, • Isaac OSMO for hybrid-cloud workflow orchestration. Mindblowing. 😮 It validates what we at Gideon have believed in for the past 7 years: the future of flexible robots will be powered by advanced visual perception and AI. If you want to build meaningful robotics companies, there’s never been a better time. And it’s never been more important to: 1. Listen to your early customers and focus on adding value to them from day one. Build long-term relationships with their People and help them solve their top problems. 2. Specialize! Focus on solving one specific problem at a time. Do not build universal platforms, trying to tackle many problems at once. When customers hear about your company, they should immediately know you’re the best in the world to solve a specific problem they have. 3. Do not reinvent the wheel; use the off-the-shelf components whenever possible. 4. Data to train your robots is key. Generalized components and platforms will always miss industry-specific data and customer insights you should have access to, so use them to build. It’s your secret superpower and a future growth flywheel. 5. Make sure your robots talk to and cooperate well with other systems. 6. Do not underestimate the complexities of deploying AI robots in the real world, especially in commercial environments. Invest in people, processes, and tools to handle this properly early on. This will make or break you. The real world is nothing like your simulation environment. 7. Partner with key industry players to accelerate your growth (like we did with Toyota Material Handling Europe.) All the building blocks are finally coming together. What is the robot you’ll start working on today? #NVIDIA #JensenHuang #Robotics #AI #AIRobotics #VisualAI #VisualPerception #ComputerVision #GTC24 #AMR #AGV #MobileRobots #HumanoidRobots

𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗘𝗿𝗮 𝗼𝗳 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 Reinforcement Learning has become the intelligence engine behind the next generation of autonomous machines. It allows robots to learn through experience, adapt to complex environments, and make decisions in real time. Researchers across the world are pushing this field forward, and the progress made between 2023 and 2025 has transformed what we thought robots could do. Modern systems now learn from high-dimensional sensory data like vision, tactile signals, and proprioception. They no longer rely on brittle rules or hand-designed controllers. Instead, they build internal models of the world and use them to plan, predict, and act with remarkable precision. Transformative breakthroughs like Dreamer world models, transformer-driven action policies, diffusion-based decision systems, and hybrid model-based control have allowed robots to move, grasp, manipulate, and navigate with a sophistication that simply didn’t exist a few years ago. Robots today learn faster, require fewer human demonstrations, and succeed in dynamic, contact-rich tasks that were once thought impossible. They can adapt their strategies on the fly when the environment changes. They can infer hidden states, anticipate future outcomes, and recover from failures with very little supervision. High-resolution tactile sensing, latent-space world models, and large-scale datasets of real robot behavior have made this evolution inevitable. Yet even with all this progress, several challenges still define the frontier. Robots must close the gap between simulation and the real world, learn to operate safely around people, build long-horizon memory, and coordinate with swarms of peers under partial observability. These problems are the heart of the next leap in autonomy. They will define which systems are capable of real mission-scale reasoning instead of short-horizon actions. The coming years will belong to hybrid systems that combine world models, foundation models, and real-time control. They will continuously update their understanding of the world as sensors age, as hardware wears, and as environments become unpredictable. They will rely on new forms of tactile intelligence, more efficient learning pipelines, and architectures that blend imagination with grounded physics. Every major advance in robotics over the past decade has moved toward one goal. Autonomy that is resilient. Autonomy that adapts. Autonomy that learns at the speed of the world itself. Singularity Systems is moving this space.

Robotics is no longer about hardware — it’s about who owns the data. When RealMan Robotics opened its data training center in Beijing, it wasn’t just creating another research hub — it was signaling the industry’s shift from hardware-driven innovation to data-driven performance. For years, robotics progress centered on building faster autonomous mobile robots (AMRs), stronger arms, and more precise sensors. But performance is hitting diminishing returns. The real bottleneck? Training data. Robots generate massive sensory streams, but without curated datasets, algorithms don’t improve. That’s why structured data pipelines have become the “conveyor belts” of the AI era. We’re seeing this across industries and geographies: • ROVR Network’s open dataset is democratizing access, much like ImageNet did for computer vision. • NVIDIA and Qualcomm are embedding compute at the edge, ensuring robots learn from local feedback in real time. • Siemens’ Shanghai plant reported a 350% efficiency leap — not from better robots, but from data-driven orchestration across fleets. • John Deere’s acquisition of GUSS Automation signals that agricultural robotics is following the same path: the value is in data loops, not just machines. The strategic play is clear: robotics differentiation is moving from hardware to data-driven adaptability. For operators, this means three things: • Data becomes the moat. Running robots today train the models that will dominate tomorrow. • Partnerships will shift. Expect joint ventures between OEMs, logistics providers, and manufacturers to pool datasets. • Competitive advantage compounds. Companies that build proprietary feedback loops will accelerate past those who just buy off-the-shelf robots. The critical question for leaders: are you just automating tasks—or building the dataset that future-proofs your operation? I put together the most comprehensive weekly newsletter on automation, robotics and innovation driving intralogistics, supply chains, and e-commerce. ↓ https://lnkd.in/evrCrS2P

Robotics and Physical AI are moving our industry beyond deterministic automation toward adaptive, physics-aware, self-optimizing production systems. A new ASME framework on Physical Artificial Intelligence for Engineering Systems formalizes the stack for this including multimodal perception, physics-grounded world models, learning-based control, simulation-to-reality transfer, and cloud–edge autonomy for real industrial environments. By integrating this stack into next-generation automation we can achieve: • Embodied AI for manipulation, dexterity, compliant control, logistics, assembly • Robotics foundation models for perception, task planning, motion generation, grasp synthesis • High-fidelity digital twins + real-time MPC + model-based RL for closed-loop optimization • Industrial Edge + deterministic control for latency-critical robotic autonomy • Safe HRC with runtime monitoring, verification, and safety-certified architectures • Autonomous, polyfunctional robotic cells capable of reconfiguration, self-calibration, and rapid changeover https://lnkd.in/ghTqd7G2 #PhysicalAI #EmbodiedAI #Robotics #IndustrialRobotics #AutonomousRobots #PolyfunctionalRobots #RobotLearning #ReinforcementLearning #FoundationModels #RoboticsFoundationModels #MultimodalPerception #3DVision #SceneUnderstanding #MotionPlanning #TrajectoryOptimization #ModelPredictiveControl #MPC #DigitalTwin #IndustrialDigitalTwin #CyberPhysicalSystems #CPS #Sim2Real #SimulationToReality #IndustrialEdge #EdgeComputing #DistributedControl #RealTimeControl #AdaptiveAutomation #FlexibleManufacturing #HighMixLowVolume #HRC #HumanRobotCollaboration #SafetyEngineering #FunctionalSafety #Verification #RuntimeMonitoring #GenerativeDesign #AutonomousMachining #PrecisionAssembly #SelfCalibration #ZeroTouchDeployment #IndustrialMetaverse #StochasticAutomation #ResilienceEngineering #Industry4_0 #Industry5_0 #SmartFactory #FutureOfManufacturing #Siemens

Teradyne Robotics pops 19% QoQ and announces its strategy In its Q4 & Full-Year 2025 earnings call, Teradyne reported that its Robotics Group grew 19% quarter over quarter, marking the third consecutive quarter of growth. More importantly, management clearly articulated why they believe that growth will continue. Straight from the earnings call: “Physical AI is already expanding the applications of advanced robotics, and we believe that trend will continue to strengthen.” And more specifically: “From a Robotics Group perspective, we expect growth tied to physical AI expanding SAM, reducing implementation complexity and continued persistent labor shortages. Our strategic pivot toward large accounts, along with a sharper focus on Ecommerce, Logistics, Semiconductor and Electronics verticals is expected to further support growth.” That’s the strategy — clearly stated. ⸻ The four markets Teradyne Robotics is targeting Ecommerce High labor intensity and constant variation. Physical AI lowers deployment friction and enables robotics to scale across large, global operators. Logistics Persistent labor shortages and repeatable workflows make this a natural fit once implementation complexity drops — especially for AMRs and cobots. Semiconductor A market Teradyne knows deeply. High reliability requirements, structured environments, and massive capacity expansion underway. Electronics Low-volume, high-mix production where Physical AI enables robots to handle variation without heavy custom engineering. ⸻ There’s a fifth market robotics companies should be pursuing aggressively: Pharmaceuticals & life sciences Why? ♦️ Over $500B is being invested in the U.S. alone, not including suppliers ♦️ Like semiconductors, this is reshoring driven by economic security, not short-term cycles ♦️ Clean, controlled environments are ideal for cobots and AMRs ♦️ Labor availability, compliance, and throughput pressures are only increasing Pharma and life sciences look a lot like semiconductor manufacturing did before automation scaled. Physical AI could accelerate adoption there just as quickly. ATI Industrial Automation is part of the UR+ ecosystem and supports Universal Robots with tool changers and robotic force sensors. #robotics #teradynerobotics

In 2022, ChatGPT transformed how we work with computers. Foundation Models for robots promise to transform how robots work with humans. Google's Gemini is yet another instantiation and a step forward in robotic capabilities. Here's a measured look at what this means: Industrial robots today have a fundamental limitation: they excel at repetition but struggle with adaptation. Gemini and other robot FM approach targets 3 persistent challenges in robotics: • Generality: Adapting to novel situations • Interactivity: Processing environmental changes • Dexterity: Improving precision in manipulation Initial tests show encouraging results: The system demonstrates better adaptability when conditions change, though real-world validation is still needed. Notable capabilities include: • Multi-step origami folding • Careful object handling • Fine-motor manipulation tasks What's particularly interesting is the cross-platform potential: • ALOHA 2 bi-arm systems • Franka research arms • Apptronik humanoids This could enable advances in: • Manufacturing processes • Healthcare assistance • Assisted living support While the results are promising, we need rigorous testing in real-world conditions. The goal isn't to replace human capabilities. Rather, it's about creating tools that enhance human potential. At Scaled Foundations, we're exploring these possibilities. We're developing infrastructure to make robotics more accessible to researchers and innovators. Because meaningful progress often comes from unexpected places. The most exciting advances might come from researchers who previously lacked access to sophisticated tools. Not for immediate transformation, but for enabling new paths of exploration. Interested in contributing to this field? Explore our GRID Platform: https://lnkd.in/gCwfMPRY

Robotics is entering a turning point - and reinforcement learning may be at the heart of it. Traditional methods teach robots with fixed rewards (“do X correctly, get Y point”). This works for simple tasks but breaks down in messy, real-world scenarios. That’s where Group Relative Policy Optimization comes in. Instead of giving a single reward, GRPO compares multiple possible outcomes of a task and ranks them. Think of it like a robot trying several approaches, then learning which was “less bad” and iterating from there - much closer to how humans refine skills. The promise is big: robots that adapt more flexibly to navigation, manipulation, or even multi robot collaboration. But there’s a catch. GRPO needs lots of trial and error runs. In simulation, this is cheap. In the real world, it’s expensive, time consuming, and sometimes unsafe. GRPO doesn’t magically solve robotics however. It does give us a training signal that looks more like how people learn - by comparing alternatives - and it scales. If we pair that with high‑throughput simulation and disciplined sim‑to‑real practice, the next wave of robot learning will look less like clever reward hacking and more like robust skill acquisition. #innovation #technology #future #management #startups