Trends in Robotics Innovation

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.

My biggest takeaways from ex-OpenAI, Apple, Meta roboticist Caitlin Kalinowski: 1. The AI frontier is shifting from digital to physical because labs see the ceiling of keyboard-bound AI. “What you can do behind a keyboard with AI is going to saturate.” Which is why labs, big tech, and startups are increasingly investing in hardware, and why enrollment at universities is rising while CS enrollment is trending down. 2. More change is coming to warfare than to consumer electronics in the next two years. Drones, robotics, and the hardware supply chain all converge on the battlefield, and Caitlin argues we have to be able to control adversarial threats to our hardware layer, not just our chatbots. 3. VR didn’t become a mainstream product, but it created the tech necessary for robotics (and war). SLAM (simultaneous localization and mapping), depth sensors, spatial computing, and understanding how humans perceive visual data in space is now powering robotics, autonomous vehicles, and drones. The technology needed to understand how a robot moves through space is essentially the same technology developed for VR headsets. 4. The hardware industry faces a looming memory crisis that could derail the robotics revolution. Memory prices are spiking—potentially doubling or more—driven by AI data center demand. Companies building consumer robotics can’t compete on price with data centers. Caitlin is advising startups to pre-buy memory and stockpile components if they can afford it, because “we are in trouble as an industry.” 5. Humanoid robots are overhyped. While humanoids are interesting for certain long-tail tasks, most manufacturing and real-world applications need dedicated robots designed for specific jobs. A robot screwing keyboards into laptop cases doesn’t need to be humanoid—it needs to be optimized for that exact task. The future will have robots for construction, electrical work, logistics, and low-volume assembly, and most won’t look humanoid. 6. Supply chain independence is a national security imperative. Over the past 25 years, essentially every layer of the hardware supply chain—from raw magnets to actuators to final assembly—has been outsourced to China, Japan, and Korea. The same actuator technology that makes a drone rotor spin also makes a robot arm move. Without an independent supply chain, the U.S. is vulnerable. As Caitlin warns, “We need to re-industrialize this country significantly in order to be safe in a military sense.” 7. Software builders don't understand how fundamentally different building hardware is. Software can compile code hourly, but in hardware, you may get only a handful of chances to “compile” before mass production, with each major build taking three to five months. Once you ship, you’re done—there are no over-the-air updates for physical components. Software intuition doesn’t transfer to hardware.

Impressive work by the new Amazon Frontier AI & Robotics team (from Covariant acquisition) and collaborators! This research enable mapping long sequences of human motion (>30 sec) on robots with various shapes as well as robots interacting with objects (box, table, etc) of different size nd in particular different from the size in the training data. This enable easier in-simulation data-augmentation and zero-shoot transfer. This is impressive and a huge potential step for reducing the need for human teleoperation data (which is hard to gather for humanoids) The dataset trajectories is available on Hugging Face at: https://lnkd.in/eygXVVHx The full code framework is coming soon. Check out the project page which has some pretty nice three.js interactive demos: https://lnkd.in/e2S-6K2T And kudos to the authors on open-sourcing the data, releasing the paper and (hopefully soon) the code. This kind of open-science projects are game changers in robotics.

Physical AI is starting to look less like robotics - and more like the next cloud infrastructure race. This week, Mind Robotics raised another $400M to build AI-powered industrial robots, pushing its valuation past $3.4B. At the same time, a new wave of embodied AI companies - from Physical Intelligence to Skild AI - are attracting capital at infrastructure-scale valuations. The narrative is shifting fast: investors are no longer underwriting “robots.” They’re underwriting foundational control systems for the physical world. What most people are missing is that hardware is becoming the distribution layer, not the moat. The real asset is the data flywheel created by real-world interaction: motion, failure, correction, adaptation. Physical AI companies are converging on the same realization that defined cloud and autonomous driving - whoever owns the operational data layer compounds fastest. That’s why simulation, deployment infrastructure, and robotics middleware are suddenly strategic assets, not support tooling. The implication for founders is clear: vertical robotics companies may struggle unless they control proprietary environments or workflows. For investors, the bigger opportunity may sit one layer below - orchestration, simulation, embodied foundation models, and industrial data infrastructure. Physical AI won’t be won by the best robot demo. It’ll be won by the company that learns fastest in the real world. #PhysicalAI #Robotics #EmbodiedAI #VentureCapital #AIInfrastructure https://lnkd.in/gUY4-Zsx

Are you ready for this future?! A future where a quiet, rubber wheeled humanoid slides next to an elder loved one (or even you) extends two telescopic arms, and lifts its passenger into a wheelchair with the grace of a seasoned nurse, minus the back strain and risk of mishaps. Engineers at Hebei University of Technology have spent years perfecting this caregiver bot. It now hoists up to 90 kg, pivots every joint with two degrees of freedom, and carries its load in one smooth, AI-balanced arc. Sensors guide each grip, lithium batteries recharge while the ward sleeps, and human staff are freed for conversation, reassurance, and real-time medical decisions instead of heavy lifting. The metallic chill we see today may fade as designers wrap these helpers in soft skins and friendly faces. Yet beneath the silicone smiles they will remain machines, lines of code driving steel and servos...only harder to notice as AI grows subtler...but maybe the extra minutes they hand back to nurses and families could make care more human, not less? How does that make you feel?

The global economy has an impending problem. While AI is compounding its ability at a historic rate, an aging population and declining fertility rates are already causing labor shortages. These trends, combined with declining costs of robotics hardware, underpin a compelling case for humanoid robots and physical AI. According to Morgan Stanley, the humanoid robot market is set to exceed $5 trillion by 2050. Even in 2025, the larger robotics space saw $21 billion of VC capital invested. And with a steady increase in patent activity mentioning “humanoid” over the past few years, these machines are already walking onto factory floors. For most of human history, productive output was a function of human muscle. Agriculture, manufacturing, logistics, and construction were all built around the physical limits of the human body. Because humans did the work, the built world standardized around human form: doorways, staircases, countertops, and tools are all designed for two arms, two legs, and hands that grip. Redesigning every factory, warehouse, and home around task-specific machines would be unfeasible. A humanoid robot that fits into existing infrastructure doesn’t need the world to change around it. Near-term use cases focus on structured, predictable settings, enabling a robot to learn quickly, make mistakes cheaply, and improve rapidly. My research team at Social Capital concluded that humanoid Robots will have the highest impact in these 7 areas: 1. Domestic Assistance: Supporting mobility needs, handling household chores, and providing medication reminders. 2. Manufacturing: Assisting assembly tasks, moving tools and parts, inspecting finished products. 3. Security & Monitoring: Patrolling facilities, investigating alerts, and assisting in emergencies. 4. Customer Service & Reception: Greeting and directing visitors, answering questions, and managing check-ins or bookings. 5. Facility Maintenance: Conducting routine inspections, performing minor repairs, cleaning, and sanitizing spaces. 6. Healthcare: Assisting nurses, delivering supplies or meals, monitoring patients. 7. Warehouse and Logistics: Picking and packing items, loading and unloading goods, and moving inventory in warehouses. By 2050, Morgan Stanley estimates that more than 1 billion humanoid robots could be working globally, with a market size of over $5 trillion. This is one of the biggest opportunities in the AI era.

Not long ago, solving a Rubik’s Cube was considered a mark of human intelligence and spatial reasoning. Can you solve the Cube that fast? Today, AI-powered robots can do it in 0.103 seconds, thanks to ultra-fast cameras capturing 4,500 frames per second and motors executing rotations in under 10 milliseconds. It’s more than a party trick — it’s a signal of how far robotics and AI have come. 📈 Processing Power: Since 2010, compute performance for AI workloads has grown by over 1 million×. ⚙️ Robotics Precision: Modern servomotors can reach accuracy levels below 5 microns, enabling surgical precision. 🧠 Learning Efficiency: Reinforcement learning models can now train 10× faster using GPU and accelerator platforms like AMD Instinct and ROCm. 🌐 Adoption Rate: Over 70% of manufacturers are investing in autonomous robotics or cobots to boost productivity and safety. The Rubik’s Cube isn’t the story — it’s the metaphor. Machines have evolved from replicating human logic to outpacing it, not through brute force but through speed, adaptability, and self-optimization. 🔹 Robots that invent their own challenges to learn faster. 🔹 AI systems that design and test hardware in simulation before humans even prototype it. 🔹 Collaborative robotics that co-create with humans — blending creativity, empathy, and logic. AI and robotics are no longer about automation; they’re about amplifying imagination. #AI #Robotics #Innovation via @cuberx5w #MachineLearning #FutureTech #Automation #ReinforcementLearning

Collaborative robots are moving automation from isolated cells into daily production activities beside human operators. Factories adopting cobots are reorganizing safety procedures and line management to gain steadier execution with less physical strain on teams. A few operational consequences are becoming visible: - Repetitive assembly tasks are shifting toward robotic support while operators focus on supervision - Flexible production lines can adapt faster to product changes through rapid robot reprogramming - Safety management is evolving with sensors and motion control integrated into daily workflows - Workforce development now requires technical skills linked to monitoring and process optimization - Stable robot movements help reduce variability and improve consistency across production cycles Long-term adoption depends on human-machine coordination and production models designed around collaboration rather than replacement. #Cobots #Industry40

🤖 Europe’s Humanoid Robotics Moment When people talk about humanoid robots, the conversation usually jumps straight to the US and China. Thinking of Europe, here’s a couple of humanoid robotics companies coming to my mind. 🇪🇸 PAL Robotics (Spain) - One of the longest-standing humanoid pioneers, focused on real-world mobility and service robotics. 🇩🇪 NEURA Robotics (Germany) - Pushing cognitive humanoids designed for industrial work and human–robot collaboration. 🇬🇧 Engineered Arts (UK) - Building the world’s most expressive humanoids for communication, interaction, and emotion-driven design. 🇬🇧 Humanoid (UK) - A rising startup reinventing how robot embodiment and AI merge. 🇳🇴 1X Technologies (Norway) - Backed by OpenAI, developing humanoids for security, logistics, and home tasks. 🇨🇭 Hexagon Robotics (Switzerland) - A Swiss player bringing precision engineering into the humanoid space. 🇬🇧 Kinisi Robotics (UK) - Designing human-adaptive movement systems for advanced mobility and assistance. 🇫🇷 Enchanted Tools (France) - Blending robotics with character-driven design to create socially intuitive, expressive humanoid helpers. 🌍 Why this matters Humanoid robots are moving from research labs to pilot projects in logistics, manufacturing, healthcare, security, hospitality, and public services. These robots unlock entirely new workflows in environments built for humans, for now. 🔍 ... meanwhile, in China… China’s government has just warned about a potential humanoid robotics bubble: too many companies, too fast, chasing hype instead of validated demand. It’s an important signal. (source: Bloomberg) My values: human-centred technology, sustainable, safe and profitable. 👉 Where do you see the strongest opportunities for humanoids deployment in Europe? Let’s open the conversation. PAL Robotics, NEURA Robotics, Engineered Arts, Humanoid, 1X, Hexagon AB, Kinisi, Enchanted Tools, Women in Robotics Switzerland, Women in Robotics, Kateryna Portmann, MBA, Nadja N., Ivo Strohhammer, Jesica Chavez 🤖🎙️, Brennand Pierce, David Reger, Jaime E. Duarte, Rohit Srivastava

AI, Robotics, and Clean Energy Are Transforming 80% of the World’s Jobs For the last few years, most discussions about AI and automation have focused on knowledge work: coders, analysts, writers, lawyers, marketers. But according to the World Economic Forum’s 2025 “Jobs of Tomorrow” report, that’s only part of the story — and increasingly, not the most important one. The real transformation is happening in the work that keeps the world running: . Agriculture . Manufacturing . Construction . Retail & Trade . Transport & Logistics . Business & Management . Healthcare Together, these seven job-families represent 80% of the global workforce — and every one of them is being reshaped by four technologies: Artificial intelligence, robotics, clean energy systems, and connected digital infrastructure. In boardrooms, AI is often discussed through a white-collar lens: productivity tools, coding assistants, or knowledge retrieval systems. But across the globe, change is already underway in very different environments. 1. In agriculture, AI-driven drones monitor crops and distribute fertilizers precisely, reducing waste and increasing yields. 2. In construction, semi-automated equipment is making sites safer and less physically demanding. 3. In healthcare, sensor networks and robotics are supporting medical teams under strain. 4. In logistics, predictive systems are optimizing routes and fuel use. 5. In retail, digital platforms and robotics are redefining supply chains and fulfillment. This isn’t a “future scenario.” It’s today — unfolding quietly, and often away from the spotlight of AI headlines. What Leaders Should Focus On 1. Bring AI strategy beyond the office. Future competitiveness depends on how technology diffuses into operations, logistics, and frontline work — not just digital functions. 2. Invest in large-scale reskilling. Skills in robotics maintenance, energy systems, and digital coordination are becoming as vital as data literacy once was. 3. Design technology with inclusion in mind. Adoption that deepens divides is bad business. Technology that expands opportunity creates resilience. 4.Build human-machine collaboration models. The goal is not substitution, but synergy — a workforce that combines human creativity with machine precision.