Innovations in Optimus Robot Development

Let’s reverse engineer Tesla Optimus humanoid robot! Part 2: Neural Architecture. Optimus is trained end-to-end: videos in, actions out. I'm quite sure it's implemented by a multimodal Transformer with the following components: (1) Image: some variant of efficient ViT, or simply an old ResNet/EfficientNet backbone (https://lnkd.in/gwJceCJu). The block pick-and-place demo doesn't require sophisticated vision. The spatial feature map from the image backbone can be tokenized easily. (2) Video: two ways. Either flatten the video into a sequence of images and produce tokens independently, or have a video-level tokenizer. There're numerous ways to efficiently process video pixel volumes. You don't necessarily need Transformer backbones, e.g. SlowFast Network (https://lnkd.in/gxZdpeB9) and RubiksNet (https://lnkd.in/gigDhJeT, my paper at ECCV 2020, efficient CUDA shift primitives). (3) Language: it's not clear if Optimus is language prompted. If it is, there needs to be a way to "fuse" the language representations into perception. FiLM is a very lightweight neural network module that serves this purpose (https://lnkd.in/gRskFhwv). You can think of it intuitively as a "cross attention" of language embedding into the image-processing neural pathway. (4) Action tokenization: Optimus needs to convert the continuous motion signals into discrete tokens for the autoregressive Transformer to work. A few ways: - Directly bin the continuous values for each hand joint control. [0, 0.01) -> token #0, [0.01, 0.02) -> token #1, etc. This is straightforward but could be inefficient due to the long sequence length. - The joint movements are highly dependent on each other, which means they occupy a low-dimensional "state space". Apply VQVAE to the motion data to obtain a shorter-length, compressed token set. (5) Putting the above pieces together, we have a Transformer controller that consumes video tokens (optionally with language modulation), and outputs action tokens, one step at a time. The next frame from the table is fed back to the Transformer, so it knows the consequence of its action. That gives the *self-corrective ability* shown in the demo. I believe the architecture is most similar to: - NVIDIA VIMA (my team’s work): https://lnkd.in/gZEDB3fD - Google RT-1: https://lnkd.in/g7N45aCU Lastly, I'm genuinely impressed by the hardware quality. The motions are fluid, and the aesthetics is amazing as well. As I mentioned above, it's such a great decision to follow human morphology closely, so that there is no gap in imitating humans. Atlas from Boston Dynamics only has simple gripper-style hands. In the long run, Optimus' bi-dexterous, 5-finger hands will prove far superior in daily tasks.

I've spent ~4 years working on the problem of locomotion and postural control of humanoid robots. Here is my take on the new Tesla Bot: Optimus Gen 2. There are a lot of little details in the video that the trained eye can unveil: On the positive side: ✅Probably trained using learning from demonstration for the movements. ✅Probably used zero-moment point (a traditional technique for locomotion). ✅The moves are extremely smooth. No video speed-up. ✅It seems the mechatronics improved a lot, there is almost no vibration. ✅10 kg total weight reduction is awesome (higher weight, bigger inertia forces, more difficult to control). ✅Good manipulation (two-hand manipulation of a fragile object is difficult). ✅Probably they use force control instead of position control (the math is more difficult, but you get a smoother control). ✅Exciting that Tesla is pushing robotics forward. Massive kudos. On the negative side: ❌It is still a static robot, as opposed to Boston Dynamics (BD) ones which are dynamic robots. ❌No jumping, no running yet. There is no balance loss at any point (because is damn difficult). ❌Going from static to dynamic movement is so difficult that BD started directly with dynamics. Optimus might need to be redesigned to achieve this. ❌There is very little information about the robot, being more transparent would be good for the industry and the scientific community. Bonus: ✅Let's not forget that Optimus is designed for mass production, this comes with many constraints. ✅Optimus is designed by a product engineer (Elon Musk), as opposed to Boston Dynamics, designed by a researcher (Marc Raibert). If I have to bet who will create a humanoid robot that I can use in my house, I bet on Elon.

Tesla Reveals Next-Gen Optimus V3 Hand in New Patent: Tendon-Driven with Forearm Actuators & 4 DoF Fingers! Tesla has just published a detailed international patent that appears to reveal the highly advanced hand design for Optimus V3. The system is a sophisticated tendon/cable-driven architecture with actuators located in the forearm (keeping the hand itself lightweight and agile). Each finger offers 4 degrees of freedom, the wrist has 2 degrees of freedom, and the entire mechanism uses just 3 thin, flexible control cables per finger running from forearm actuators through the wrist into the fingers. Advanced wrist routing cleverly switches cables from a lateral stack on the forearm side to a vertical stack on the hand side, with a special transition zone that minimizes stretch, torque, friction, and crosstalk during complex yaw/pitch movements. The design is clearly optimized for mass production with simplified parts and efficient assembly. This patent shows Tesla is solving one of the hardest problems in humanoid robotics — giving Optimus truly human-like dexterity while keeping costs low for high-volume scaling. The future of capable, affordable humanoid robots is getting very real! #Tesla #Optimus #OptimusV3 #TeslaOptimus #HumanoidRobot #ElonMusk #RobotHand #PhysicalAI #TendonDriven #FutureOfRobotics

Tesla has published a new patent detailing a knee joint assembly for Optimus that is directly modeled on human biological anatomy. The patent breaks down how the human knee uses a quadriceps tendon, patella, and patellar ligament system to convert muscle force into powerful bending motion, and then describes how Optimus replicates this using a 4-bar mechanical linkage that mimics the exact same movement pattern. The result is a knee that allows the robot's lower leg to rotate approximately 150 degrees from straight, matching the full range of motion found in a human knee. This enables Optimus to perform natural human movements like walking, squatting, climbing stairs, and kneeling, all of which require the kind of fluid knee articulation that most robotic systems struggle to achieve.

Tesla Optimus — New Generation Revealed by Elon Musk. Elon Musk has unveiled Optimus 3, the newest generation of Tesla’s humanoid robot, signaling a major step toward practical robotics deployment. Unlike earlier prototypes, Optimus 3 is being positioned as a system designed for real-world work, starting inside Tesla’s own factories. According to Musk, the new generation focuses on three critical improvements: 1. Higher Autonomy Optimus 3 can execute more complex, multi-step tasks with reduced human supervision, powered by Tesla’s AI training stack. 2. Improved Dexterity The robot’s hands and manipulation systems are designed to handle precise tasks that were previously difficult for humanoid robots. 3. Real-World Deployment Tesla’s strategy is to deploy Optimus internally first, allowing the robots to learn directly from factory environments before broader commercialization. This approach mirrors Tesla’s playbook in autonomous driving: deploy early, collect massive real-world data, and iterate rapidly. If successful, Tesla could accumulate millions of hours of robotic training data, creating a significant advantage in the emerging humanoid robotics market. The broader implication is clear. Humanoid robots are moving from experimental prototypes to operational tools — potentially transforming industries such as manufacturing, logistics, and services. The key question now is not whether humanoid robots will arrive. It is how quickly companies like Tesla can scale them. #AI #Robotics #Tesla #Optimus3 #Automation #FutureOfWork

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱𝘀 𝗮𝗿𝗲 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴. 𝗡𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲𝘆 𝗹𝗼𝗼𝗸 𝗵𝘂𝗺𝗮𝗻, 𝗯𝘂𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 ��𝗹𝗿𝗲𝗮𝗱𝘆 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗳𝗼𝗿 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻. Consumer humanoids must handle open-ended chaos. Industrial humanoids get stations, fixtures, takt times, and SOPs — a much narrower software problem. And one company has a structural advantage nobody else can match: 𝗧𝗲𝘀𝗹𝗮 ’𝘀 𝗢𝗽𝘁𝗶𝗺𝘂𝘀. Because Optimus trains inside Tesla’s own factories, it gets immediate deployment environments, continuous data and fast iteration, zero negotiation with integrators, alignment with real production bottlenecks. And soon, a boost from Grok for perception and control. That tight feedback loop is a moat — and likely gives Optimus the fastest time-to-market in the category. The rest of the field has friction: manufacturers, integrators, safety approvals, IT, pilot cycles. Access slows everything. There is one player that might be closing the gap: 𝗔𝗴𝗶𝗹𝗲 𝗥𝗼𝗯𝗼𝘁𝘀. Today’s acquisition of ThyssenKrupp Automotive Engineering gives them something precious: Real car factory access, engineering integration, and validation environments. This arguably puts Agile in the #2 slot for industrial humanoids — not on hardware, but on access. 𝘈𝘯𝘥 𝘪𝘯 𝘩𝘶𝘮𝘢𝘯𝘰𝘪𝘥 𝘳𝘰𝘣𝘰𝘵𝘪𝘤𝘴, 𝘢𝘤𝘤𝘦𝘴𝘴 𝘪𝘴 𝘢 𝘬𝘦𝘺 𝘢𝘥𝘷𝘢𝘯𝘵𝘢𝘨𝘦 The next 12–24 months will show whether these robots move from hype to a real industrial platform. My bet: The winners will be the ones with the tightest data loops, not the most elegant mechatronics. Tesla Grok #Optimus Agile Robots SE thyssenkrupp Automation Engineering

Tesla Optimus: The US Humanoid Robotics Bet that Changes Everything The United States is betting on AI, autonomy, and frontier R&D, placing the US market at the center of the next industrial transformation: embodied intelligence. The core of this strategy is Tesla Optimus Gen 3, expected to premiere in early 2026. The Technological Opportunity: AI First Optimus is an AI platform designed as a universal worker. It achieves Self-Learning Autonomy by using end-to-end neural networks (like FSD) to process visual data and learn tasks simply by observing a human, the "see, understand, do" approach. Precision & Scale: Optimus features highly sophisticated hands with 22 DOFs in the hand (25 DOFs total). Musk plans production in the millions of units annually. Utilizing Gigafactories and vertical integration, the price target is $20,000 to $30,000. Techno-Political & Societal Implications The ambition behind Optimus raises profound questions about control and competition: Cybernetic Control: Future plans include Neuralink chip integration, enabling users to control Optimus with general thought. With planned bidirectional feedback, the robot becomes a "suit only made of titanium and silicon", a true cybernetic avatar. Workforce Revolution: Optimus is designed to be a universal worker, capable of cooking, cleaning, and driving a car. This massive deployment is expected to fundamentally transform labor and household chores. Global Rivalry: While the U.S. leads in AI, it faces a strategic vulnerability in scaling factory adoption compared to China, which rapidly deploys humanoids in industrial settings. The US pursuit of an "open, distributed ecosystem" hinges on Tesla's ability to achieve mass production. Governance Risk: Musk has publicly tied his pursuit of significant voting control in Tesla to his development of this "massive robot army," underscoring the strategic control risks associated with such powerful platforms. The US must rapidly convert its AI lead into affordable, scalable hardware to maintain global competitiveness. The question is no longer if machines will work alongside humans, but "where and under whose flag they will do it". What are your biggest concerns, societal acceptance, geopolitical rivalry, or ethical control, as Optimus approaches its expected 2026 premiere? #Tesla #Optimus #HumanoidRobots #AI #RoboticsRace #USInnovation #TechnoPolitics #FutureOfWork #Neuralink #EmbodiedIntelligence