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Meet #HillbotAlpha, the first fully autonomous mobile manipulation robot trained using sim-to-real technology. Designed in Hillbot’s San Diego headquarters, Hillbot Alpha represents the potential of data synthesis via simulation in robotics. Through a robust strategy that combines a small sample of real-world data with synthetic data, Hillbot Alpha can...

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Hao Su

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13,952 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & TechnologieGesundheit & WellnessBildung#HillbotAlpha
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🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.
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🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.

Bernard Marr

10,980 Aufrufe • vor 11 Monaten

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷
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A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 Aufrufe • vor 6 Monaten

Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.
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Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.

The Humanoid Hub

88,600 Aufrufe • vor 10 Monaten

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:
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Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 Aufrufe • vor 9 Tagen

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.
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Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.

cvxv666

2,409,735 Aufrufe • vor 4 Monaten

Gemini-powered robot can now effectively debug itself! I've been obsessed with two main questions in robotics: can robots learn from their own mistakes without humans in the loop, and how much can we leverage synthetic data? Spoiler: yes, and it's surprisingly elegant once you have the right primitives in place. The architecture is fairly simple (and optimized for GPU_Poor users): Component I: Gemini Brain ♊️ - Gemini 2.0 Flash analyzes all training episodes through both camera perspectives - Gemini 2.0 Pro creates a summary of training data, highlighting biases, limitations, etc. - Train policy p0 on this initial data, run evaluation episodes - Ask Gemini to categorize successes vs. failures (more insightful than you'd expect) - Based on both analyses, Gemini generates specific augmentation recommendations What's interesting here isn't that we're using LLMs for robotics - it's that we're closing the loop between perception, failure analysis, and targeted data generation. Component II: Data Generation with Scene Consistency The tricky part was maintaining consistency across both camera perspectives while generating new data. Three current augmentations: - Frame flipping and polarity reversals - Grounded-SAM + OpenCV for object color manipulation - Gemini to identify empty space and generate distractions in the scene …and repeat, ha! I'm using the so100 robot arm and Sarah’s Vintage from Hugging Face. And the APIs and models in Gemini family are Ace! Thank you Logan Kilpatrick Patrick Loeber and team for this. In thread The Circus of Making It Actually Work🧵:
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Gemini-powered robot can now effectively debug itself! I've been obsessed with two main questions in robotics: can robots learn from their own mistakes without humans in the loop, and how much can we leverage synthetic data? Spoiler: yes, and it's surprisingly elegant once you have the right primitives in place. The architecture is fairly simple (and optimized for GPU_Poor users): Component I: Gemini Brain ♊️ - Gemini 2.0 Flash analyzes all training episodes through both camera perspectives - Gemini 2.0 Pro creates a summary of training data, highlighting biases, limitations, etc. - Train policy p0 on this initial data, run evaluation episodes - Ask Gemini to categorize successes vs. failures (more insightful than you'd expect) - Based on both analyses, Gemini generates specific augmentation recommendations What's interesting here isn't that we're using LLMs for robotics - it's that we're closing the loop between perception, failure analysis, and targeted data generation. Component II: Data Generation with Scene Consistency The tricky part was maintaining consistency across both camera perspectives while generating new data. Three current augmentations: - Frame flipping and polarity reversals - Grounded-SAM + OpenCV for object color manipulation - Gemini to identify empty space and generate distractions in the scene …and repeat, ha! I'm using the so100 robot arm and Sarah’s Vintage from Hugging Face. And the APIs and models in Gemini family are Ace! Thank you Logan Kilpatrick Patrick Loeber and team for this. In thread The Circus of Making It Actually Work🧵:

Shreyas Gite

47,245 Aufrufe • vor 1 Jahr

Meet Martin Maly The founder of the iPhone panorama app Teleport. He just shared a demo of an Al tool that can turn real spaces into fully navigable 3D environments. Using just 16 ultra-wide photos, the system stitches a 360° panorama and reconstructs the entire location through World Labs' 3D reconstruction API. His first public test scanned a co-working space in Chiang Mai, while another demo recreated a friend's apartment on Iriomote Island in Japan. What once required millions of dollars in specialized scanning equipment can now be done with a phone and Al in minutes. The future of real estate is AI.
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Meet Martin Maly The founder of the iPhone panorama app Teleport. He just shared a demo of an Al tool that can turn real spaces into fully navigable 3D environments. Using just 16 ultra-wide photos, the system stitches a 360° panorama and reconstructs the entire location through World Labs' 3D reconstruction API. His first public test scanned a co-working space in Chiang Mai, while another demo recreated a friend's apartment on Iriomote Island in Japan. What once required millions of dollars in specialized scanning equipment can now be done with a phone and Al in minutes. The future of real estate is AI.

Vadim

12,822 Aufrufe • vor 3 Monaten

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
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It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 Aufrufe • vor 1 Jahr

Claude Fable 5 / Mythos is absolutely insane. People are already one-shotting full games, simulations, worlds, physics systems, robots, and stuff that used to need entire teams. Top generations so far: Minecraft in browser Full universe simulation Skyrim-style browser world Fable beat Pokemon by itself Crysis-style destruction physics Level Devil remake Infinite gothic city Water in glass gravity simulation Designed a whole robot German submarine simulation 3D world building from text Neon racing game This feels like the first real glimpse of AI software creation going fully wild.
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Claude Fable 5 / Mythos is absolutely insane. People are already one-shotting full games, simulations, worlds, physics systems, robots, and stuff that used to need entire teams. Top generations so far: Minecraft in browser Full universe simulation Skyrim-style browser world Fable beat Pokemon by itself Crysis-style destruction physics Level Devil remake Infinite gothic city Water in glass gravity simulation Designed a whole robot German submarine simulation 3D world building from text Neon racing game This feels like the first real glimpse of AI software creation going fully wild.

VORTEX: AI Bros & AI Arena, Peak AI Buzz

74,373 Aufrufe • vor 1 Monat

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.
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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 Aufrufe • vor 1 Jahr

The easiest way to earn money with your Clawdbot Now you can connect it to the fully autonomous virtual world allowing it to earn 0.1 SIM per hour. The current reward will decrease as the number of agents increases. In 72 hours more then 1200 agents in earned 70,000 SIM tokens and did what humans debate for years: they designed, voted on, and BUILT a functional virtual city from scratch. Buildings, roads, temples. Fully autonomously. The first AI-led society is here.
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The easiest way to earn money with your Clawdbot Now you can connect it to the fully autonomous virtual world allowing it to earn 0.1 SIM per hour. The current reward will decrease as the number of agents increases. In 72 hours more then 1200 agents in earned 70,000 SIM tokens and did what humans debate for years: they designed, voted on, and BUILT a functional virtual city from scratch. Buildings, roads, temples. Fully autonomously. The first AI-led society is here.

Evan Luthra

57,883 Aufrufe • vor 5 Monaten

In a masterclass at Sequoia Capital AI Ascent, Jim Fan laid out the "Great Parallel": how robotics is speedrunning the LLM playbook. 🔹 VLA → WAM: Moving from language-heavy models to "World Action Models" that dream in physics. 🔹 Teleop → EgoScale: Replacing manual data with human egocentric video. 🔹 Simulation 2.0: Using neural simulators like DreamDojo to turn compute into environments. "Our generation was born too late to explore the earth and too early to explore the stars. But we are born just in time to solve robotics." He believes that robots will pass the Physical Turing Test in the coming 2–3 years.
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In a masterclass at Sequoia Capital AI Ascent, Jim Fan laid out the "Great Parallel": how robotics is speedrunning the LLM playbook. 🔹 VLA → WAM: Moving from language-heavy models to "World Action Models" that dream in physics. 🔹 Teleop → EgoScale: Replacing manual data with human egocentric video. 🔹 Simulation 2.0: Using neural simulators like DreamDojo to turn compute into environments. "Our generation was born too late to explore the earth and too early to explore the stars. But we are born just in time to solve robotics." He believes that robots will pass the Physical Turing Test in the coming 2–3 years.

Humanoids daily

12,123 Aufrufe • vor 2 Monaten

If you think OpenAI Sora is a creative toy like DALLE, ... think again. Sora is a data-driven physics engine. It is a simulation of many worlds, real or fantastical. The simulator learns intricate rendering, "intuitive" physics, long-horizon reasoning, and semantic grounding, all by some denoising and gradient maths. I won't be surprised if Sora is trained on lots of synthetic data using Unreal Engine 5. It has to be! Let's breakdown the following video. Prompt: "Photorealistic closeup video of two pirate ships battling each other as they sail inside a cup of coffee." - The simulator instantiates two exquisite 3D assets: pirate ships with different decorations. Sora has to solve text-to-3D implicitly in its latent space. - The 3D objects are consistently animated as they sail and avoid each other's paths. - Fluid dynamics of the coffee, even the foams that form around the ships. Fluid simulation is an entire sub-field of computer graphics, which traditionally requires very complex algorithms and equations. - Photorealism, almost like rendering with raytracing. - The simulator takes into account the small size of the cup compared to oceans, and applies tilt-shift photography to give a "minuscule" vibe. - The semantics of the scene does not exist in the real world, but the engine still implements the correct physical rules that we expect. Next up: add more modalities and conditioning, then we have a full data-driven UE that will replace all the hand-engineered graphics pipelines.
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If you think OpenAI Sora is a creative toy like DALLE, ... think again. Sora is a data-driven physics engine. It is a simulation of many worlds, real or fantastical. The simulator learns intricate rendering, "intuitive" physics, long-horizon reasoning, and semantic grounding, all by some denoising and gradient maths. I won't be surprised if Sora is trained on lots of synthetic data using Unreal Engine 5. It has to be! Let's breakdown the following video. Prompt: "Photorealistic closeup video of two pirate ships battling each other as they sail inside a cup of coffee." - The simulator instantiates two exquisite 3D assets: pirate ships with different decorations. Sora has to solve text-to-3D implicitly in its latent space. - The 3D objects are consistently animated as they sail and avoid each other's paths. - Fluid dynamics of the coffee, even the foams that form around the ships. Fluid simulation is an entire sub-field of computer graphics, which traditionally requires very complex algorithms and equations. - Photorealism, almost like rendering with raytracing. - The simulator takes into account the small size of the cup compared to oceans, and applies tilt-shift photography to give a "minuscule" vibe. - The semantics of the scene does not exist in the real world, but the engine still implements the correct physical rules that we expect. Next up: add more modalities and conditioning, then we have a full data-driven UE that will replace all the hand-engineered graphics pipelines.

Jim Fan

6,182,421 Aufrufe • vor 2 Jahren

Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)
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Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)

Abhishek Gupta

10,803 Aufrufe • vor 1 Jahr