Humanoid Robot Development

We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate. Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution. Our recipe is called "EgoScale": - Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks. - Most surprising result: a *single* teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency. - Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone. The scalable path to robot dexterity was never more robots. It was always us. - Website: https://lnkd.in/gxzgeP-2 - Paper: https://lnkd.in/g7PJdz_8

In just ONE year, humanoid robots at the CCTV Spring Festival Gala went from “cool machines” to something that felt… human. What do you think? 2025 → 2026. The difference? Not incremental. Exponential. What changed in 12 months? 📊 The Data Behind the Leap: • AI model capability has been doubling at unprecedented rates (training compute for frontier models has grown >10x in short cycles). • Latency in edge AI systems is now measured in single-digit milliseconds — enabling real-time motion response. • Actuator precision and torque density in humanoid robotics improved significantly, enabling smoother micro-movements. • Multimodal AI (vision + audio + spatial awareness) accuracy has crossed 90%+ benchmarks in controlled environments. • Reinforcement learning in simulation can now compress “years” of physical training into weeks. Result? 2025: Pre-programmed choreography. 2026: Real-time adaptive interaction. We are witnessing the shift from: 🔹 Robots as automation to 🔹 Robots as embodied AI platforms And here’s the bigger implication: When physical AI converges with high-performance edge compute, robotics stops being hardware-centric… and becomes software-defined. The real revolution isn’t the robot you saw on stage. It’s the AI stack running inside it. If this is the progress visible in public within 12 months, imagine what’s happening inside R&D labs right now. Humanoids are no longer a science experiment. They are becoming infrastructure. 2026 is the year robotics started to feel personal. #AI #Robotics #PhysicalAI #Humanoids #DeepLearning #EdgeAI #Innovation

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.

Could AI Robots Help Fill the Labor Gap? As a futurist field, embodied AI—also known as humanoids—is captivating. Labor shortages spurred by long-term demographic shifts, coupled with advances in generative AI, are accelerating the commercialization of robots designed to emulate human behavior. The global economy faces labor shortages due to demographic trends that may hinder growth for years. Concurrently, advancements in large language models and generative AI are poised to drive transformative innovations across various industries, from healthcare to manufacturing. These trends are likely to fuel the development of humanoids—advanced robots equipped with limbs and AI-powered "brains." The adoption of these humanoid robots might outpace that of autonomous vehicles, presenting significant opportunities for investors in companies developing these robots and their components, and industries integrating them into their workforce. Its worth noting that Adam Jonas, Head of Global Autos and Shared Mobility research at Morgan Stanley, notes the adaptability of humanoids: "Consider the vast array of tasks humans perform using just our hands or tools, and the numerous machines tailored for human dexterity. As the growth of the working-age population in advanced economies continues to decline, humanoids could become essential for industries struggling to attract sufficient labor to maintain productivity." Morgan Stanley analysts project that by 2040, the U.S. alone could have 8 million working humanoid robots, impacting wages by $357 billion. By 2050, this number could rise to 63 million, potentially affecting 75% of occupations, 40% of employees, and approximately $3 trillion in payroll. "The commercialization of humanoid robots will encounter significant challenges, particularly in gaining social and political acceptance, given their potential to disrupt a large portion of the global workforce," says Jonas. He highlights that up to 70% of construction jobs and 67% in farming, fishing, and forestry could be impacted. "While they may not be the ideal solution, they are an increasingly necessary one for a world facing significant longevity challenges." #HumanoidRobots #AILaborSolutions #FutureOfWork #LaborShortage #GenerativeAI #RoboticsInnovation #AIInvestment #EconomicGrowth #TechTrends #WorkforceTransformation #futures

🔥 Introducing Physical AI workbench, how Voxel51 and NVIDIA are solving the data pipeline bottleneck that’s blocking Physical AI at scale. 🔥 As autonomous vehicles, humanoid and industrial robots move from lab to deployment, teams need high-fidelity simulations to deploy with confidence. However, over 50% of Physical AI simulations fail because of bad input data, slowing teams and wasting millions in compute costs. ⚠️ The problem: Physical AI systems process petabytes of multimodal sensor data—LiDAR, radar, cameras, IMU. Even a small calibration or timing error between LiDAR, radar, and camera sensors can ripple downstream, resulting in inaccurate neural reconstructions and wasted compute dollars. ✅ The solution: Physical AI Workbench integrates with NVIDIA Omniverse NuRec and NVIDIA Cosmos, giving teams a standardized way to audit, enrich, and prepare multimodal data for simulation and neural reconstruction, ensuring every test starts with trusted data. How it works: 🔍 Catch and fix errors automatically by auditing sensor data across 75+ critical checkpoints 🔧 Transform raw sensor streams into structured, searchable data with AI data enrichment ⚡ Trigger neural reconstructions and generate synthetic scene variations 📈 Scale simulation workflows with complete traceability and speed Automated QA checks enable teams to catch data quality issues before wasting valuable compute resources, prevent downstream failures, and increase simulation ROI. Walking the floor at #NVIDIAGTC DC last week, the signal for Physical AI is stronger than ever. If you're building AVs NVIDIA DRIVE, humanoid robots NVIDIA Robotics, manufacturing automation—this is the data engine you need between your sensors and your simulations. 🔗 See how it works here: https://lnkd.in/e2zgyx7K

Everyone looks at China’s robot training centers and says: “That’s just a transition phase.” True. But also dangerously comforting. Because transition phases lock in mental models. What we’re calling “automation” right now is, in reality, human labor reorganized around machines. Rows of people correcting robots, labeling edge cases, feeding learning loops. Not replacing work — subordinating it. Yes, this phase will pass. Machines will need fewer humans over time. But the logic we establish now tends to persist. The real learning isn’t about robotics. It’s this: technology trajectories don’t just emerge — they are designed, normalized, and justified early. If we accept that humans are merely temporary training data until they’re no longer needed, we shouldn’t be surprised by where this ends. The strategic question leaders should ask isn’t “How fast will this phase be over?” It’s “What human role are we defining as acceptable while we’re in it?” Because transition phases don’t disappear. They harden into precedent. https://lnkd.in/eXqgYAcK #AI #Transformation #Robotics #Change

The global humanoid robot race is heating up—and China isn't just joining; it's aiming to lead. Companies like UBTECH Robotics, CloudMinds Technology Inc., Fourier Intelligence, XPENG Robotics, LEJU ROBOTICS , Robot Era (Xing Era), LimX Dynamics, Zhiyuan Robotics (AgiBot（智元机器人）), Unitree Robotics, EXRobots , and Turing Robot are attracting billions in investment, launching robots that can run, jump, climb stairs, and even perform industrial tasks. While Boston Dynamics and Tesla's Optimus dominate the headlines, few realize that Chinese humanoids like UBTech’s Walker, Fourier’s GR-1, and Xpeng’s Iron are already handling complex real-world tasks—from assembling EVs in factories to rehabilitation assistance. Companies like LimX Dynamics and Zhiyuan Robotics are even integrating advanced AI like Large Language Models (LLMs) into humanoids, making them smarter, more adaptable, and potentially far more useful. Should we embrace or fear China’s rapid advancements in humanoid robotics? Western narratives often downplay these breakthroughs, focusing instead on familiar names closer to home. But ignoring China’s robot revolution could be a strategic mistake. Are we ready for a future where the leading humanoid brands and the most advanced robotics technologies might not come from the West, but from Chinese companies backed by Alibaba, Tencent, and even state investors?

Bipedal walking is an incredible engineering challenge, but in a flat warehouse or factory, it is often a massive waste of compute and battery life. We spend huge amounts of energy and control processing just to keep a bipedal robot from falling over. Every joule spent on maintaining balance is a joule stolen from payload capacity and manipulation tasks. This is why the pivot toward wheeled humanoids—seen recently with new platforms from Unitree and others—is the most pragmatic architectural trend in robotics right now. A wheeled base combined with a humanoid torso solves the immediate constraints of industrial deployment. You maintain the anthropomorphic upper body needed to train generalized AI on human manipulation data, but you gain the energy efficiency, payload stability, and speed of a traditional AMR. Let the platform roll efficiently to the workstation, then use the humanoid upper body to execute the complex picking. Form factor should always follow physics, not science fiction. #Robotics #PhysicalAI #Automation #Engineering #HardwareArchitecture

A #humanoid #robot has started working inside a car factory. This is a real milestone. For the first time publicly, a humanoid robot is performing autonomous work inside a live #automotive #manufacturing environment rather than a research lab or a controlled test facility. Boston Dynamics #Atlas is being trialed inside a Hyundai Motor Group plant, where it is handling real production tasks such as sorting and moving automotive components. This matters because Atlas is not a traditional #industrial robot. It is a fully electric, bipedal humanoid designed to walk through human workspaces, perceive its surroundings, and manipulate objects using arms and hands. The latest version features a newly developed three finger industrial hand with integrated sensing, designed specifically for robustness and repeatability in factory conditions. Earlier humanoid robots demonstrated impressive motion and balance, but they rarely crossed into real production settings. In this case, Atlas is operating autonomously within a functioning factory, performing a useful task that fits into an existing workflow. This does not mean humanoids are ready to replace large parts of the #workforce. It does mean that robots are beginning to adapt to human factories rather than the other way around. That shift is subtle, but it is fundamental.

Humanoids just got a human sense of touch — and it’s a game-changer. We’ve all seen robots walk, grasp, and even dance. But third-generation humanoids like Optimus, Figure, and XPENG are now moving from “motion” to “feeling.” The breakthrough? Ultra-thin tactile electronic skin — flexible fabrics thinner than 0.2 mm that can be tailored like a custom suit over the robot’s body. These aren’t simple pressure sensors. They detect gram-level touches, sense textures, feel objects slipping before they drop, and even map warmth and pressure in real time. Watch the demo 👇 A robot gets patted on the shoulder, hugged, and responds with live pressure mapping. The same fabric tech works as a smart mat that instantly visualizes every touch on a laptop screen. Why it matters: • Industrial dexterity jumps to a new level (no more dropped boxes) • Robots become safe enough for homes, hospitals, and eldercare • High-density sensor arrays (dozens per cm²) are now the new standard Market projection: The global flexible sensor industry is headed toward ~$4 billion by 2030. This is the final piece that turns robots from tools into true collaborative partners. The future isn’t just smarter robots — it’s robots that feel.