Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Introducing PROWL! We’ve built RL agents that explore game environments, tasked with discovering failures in world models across physics, visuals, and actions. Those failures then become training data in an automated loop that advances world model performance.

Odyssey

32,269 subscribers

62,431 görüntüleme • 26 gün önce •via X (Twitter)

Oyun Eğitim Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Yann LeCun says that within a year to 18 months, we'll have a general method for training hierarchical world models These models would learn from video and real-world data, then help plan actions in robotics, healthcare, and other areas "then scale them toward a universal world model"

Yann LeCun says that within a year to 18 months, we'll have a general method for training hierarchical world models These models would learn from video and real-world data, then help plan actions in robotics, healthcare, and other areas "then scale them toward a universal world model"

Haider.

200,858 görüntüleme • 21 gün önce

Introducing a world built by the Moonlake's world model. 🏙️ Most world models only allow for a limited action space. Moonlake maintains multimodal states across physics, appearance, geometry, and casual effects and predict how they evolve under different actions. 👇

Introducing a world built by the Moonlake's world model. 🏙️ Most world models only allow for a limited action space. Moonlake maintains multimodal states across physics, appearance, geometry, and casual effects and predict how they evolve under different actions. 👇

Moonlake

379,102 görüntüleme • 3 ay önce

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence, frontier labs can train on RL environments across every subject matter all in one place.

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence, frontier labs can train on RL environments across every subject matter all in one place.

Ali Ansari

332,396 görüntüleme • 6 ay önce

It’s time to go beyond language models. Introducing Odyssey-2 Max, our most powerful world model yet. It materially advances the SOTA in physical accuracy. This is a big step toward models that simulate and interact with the world in real time. A new intelligence entirely!

It’s time to go beyond language models. Introducing Odyssey-2 Max, our most powerful world model yet. It materially advances the SOTA in physical accuracy. This is a big step toward models that simulate and interact with the world in real time. A new intelligence entirely!

Odyssey

2,178,933 görüntüleme • 1 ay önce

Introducing Genie 2: our AI model that can create an endless variety of playable 3D worlds - all from a single image. 🖼️ These types of large-scale foundation world models could enable future agents to be trained and evaluated in an endless number of virtual environments. →

Introducing Genie 2: our AI model that can create an endless variety of playable 3D worlds - all from a single image. 🖼️ These types of large-scale foundation world models could enable future agents to be trained and evaluated in an endless number of virtual environments. →

Google DeepMind

1,440,676 görüntüleme • 1 yıl önce

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

Ali Ansari

206,727 görüntüleme • 2 ay önce

Introducing W&B Skills for coding agents! Watch us install the skill, point it at a live fine-tuning project with thousands of traces and RL training runs, then query it all from the terminal in seconds. Also works with W&B Weave to pull in your agent traces and spot failures.

Introducing W&B Skills for coding agents! Watch us install the skill, point it at a live fine-tuning project with thousands of traces and RL training runs, then query it all from the terminal in seconds. Also works with W&B Weave to pull in your agent traces and spot failures.

Weights & Biases

255,658 görüntüleme • 2 ay önce

Happy to share our new work on Navigation World Models! 🔥🔥 Navigation is a fundamental skill of agents with visual-motor capabilities. We train a single World Model across multiple environments and diverse agent data. w/ Gaoyue Zhou, Danny Tran, trevordarrell and Yann LeCun.

Happy to share our new work on Navigation World Models! 🔥🔥 Navigation is a fundamental skill of agents with visual-motor capabilities. We train a single World Model across multiple environments and diverse agent data. w/ Gaoyue Zhou, Danny Tran, trevordarrell and Yann LeCun.

Amir Bar

83,464 görüntüleme • 1 yıl önce

Humans learn and improve from failures. Similarly, foundation models adapt based on human feedback. Can we leverage this failure understanding to enhance robotics systems that use foundation models? Introducing AHA—a vision-language model for detecting and reasoning over failures in robotic manipulation. Project page: 🧵Thread👇 Aha!

Humans learn and improve from failures. Similarly, foundation models adapt based on human feedback. Can we leverage this failure understanding to enhance robotics systems that use foundation models? Introducing AHA—a vision-language model for detecting and reasoning over failures in robotic manipulation. Project page: 🧵Thread👇 Aha!

Jiafei Duan

48,739 görüntüleme • 1 yıl önce

A reward model that works, zero-shot, across robots, tasks, and scenes? Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories. Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more! 🧵 (1/12)

A reward model that works, zero-shot, across robots, tasks, and scenes? Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories. Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more! 🧵 (1/12)

Jesse Zhang @ ICRA 2026 ✈️

99,385 görüntüleme • 3 ay önce

Google DeepMind researchers say world models allow robotic agents to learn and plan in simulated environments, cutting real-world risks and costs. Robots can also potentially experience and learn rare scenarios in a world model, like Halloween crowds.

Google DeepMind researchers say world models allow robotic agents to learn and plan in simulated environments, cutting real-world risks and costs. Robots can also potentially experience and learn rare scenarios in a world model, like Halloween crowds.

The Humanoid Hub

30,148 görüntüleme • 9 ay önce

Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more

Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more

Prime Intellect

1,132,472 görüntüleme • 6 ay önce

What if robots could improve themselves by learning from their own failures in the real-world? Introducing 𝗣𝗟𝗗 (𝗣𝗿𝗼𝗯𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗗𝗶𝘀𝘁𝗶𝗹𝗹) — a recipe that enables Vision-Language-Action (VLA) models to self-improve for high-precision manipulation tasks. PLD couples real-world residual reinforcement learning with standard supervised fine-tuning — letting robots discover, recover, and distill their own data flywheel. Quick 🧵

What if robots could improve themselves by learning from their own failures in the real-world? Introducing 𝗣𝗟𝗗 (𝗣𝗿𝗼𝗯𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗗𝗶𝘀𝘁𝗶𝗹𝗹) — a recipe that enables Vision-Language-Action (VLA) models to self-improve for high-precision manipulation tasks. PLD couples real-world residual reinforcement learning with standard supervised fine-tuning — letting robots discover, recover, and distill their own data flywheel. Quick 🧵

Wenli Xiao

184,370 görüntüleme • 7 ay önce

Weekends are for research. Inside OptimAI labs, we’ve been pushing agents beyond the screen. A first glimpse of OptimAI Prime. Not just agents that think, but agents that operate. Learning through reinforcement, coordinating across the network, and executing in real-world environments. From digital workflows → to physical actions. This is the next step for the agent-native internet.🤖 Still early. But already in motion.

Weekends are for research. Inside OptimAI labs, we’ve been pushing agents beyond the screen. A first glimpse of OptimAI Prime. Not just agents that think, but agents that operate. Learning through reinforcement, coordinating across the network, and executing in real-world environments. From digital workflows → to physical actions. This is the next step for the agent-native internet.🤖 Still early. But already in motion.

OptimAI Network

20,455 görüntüleme • 1 ay önce

Every roboticist knows the pain of "Day 1." Real-world training: ❌ Fragile hardware & constant failures ❌ High latency & slow iteration ❌ Narrow data diversity Axis is breaking the cycle. We’ve built the first browser-based, infra-level platform that decouples assets, tasks, and high-fidelity rendering. Through our protocol-level abstraction, we’re moving beyond simple simulation to a unified data engine: ✅ Low-barrier collection with infinite data diversity. ✅ Cross-simulator operations, unified. ✅ End-to-end acceleration: Task -> Data -> Train -> Deployment in one loop. Coming soon.

Every roboticist knows the pain of "Day 1." Real-world training: ❌ Fragile hardware & constant failures ❌ High latency & slow iteration ❌ Narrow data diversity Axis is breaking the cycle. We’ve built the first browser-based, infra-level platform that decouples assets, tasks, and high-fidelity rendering. Through our protocol-level abstraction, we’re moving beyond simple simulation to a unified data engine: ✅ Low-barrier collection with infinite data diversity. ✅ Cross-simulator operations, unified. ✅ End-to-end acceleration: Task -> Data -> Train -> Deployment in one loop. Coming soon.

Axis Robotics

39,215 görüntüleme • 4 ay önce

🚀 Training AI agents isn’t about teaching them which buttons to click. It’s about judgment. Labelbox builds RL environments with domain experts, creating thousands of real-world scenarios where AI agents learn how to handle complex decisions across industries.

🚀 Training AI agents isn’t about teaching them which buttons to click. It’s about judgment. Labelbox builds RL environments with domain experts, creating thousands of real-world scenarios where AI agents learn how to handle complex decisions across industries.

Labelbox

22,158,127 görüntüleme • 1 ay önce

Today we're announcing cua-bench: a framework for benchmarking, training data, and RL environments for computer-use AI agents. Why? Current agents show 10x variance across minor UI changes. Here's how we're fixing it.

Today we're announcing cua-bench: a framework for benchmarking, training data, and RL environments for computer-use AI agents. Why? Current agents show 10x variance across minor UI changes. Here's how we're fixing it.

Cua

189,503 görüntüleme • 5 ay önce

Wow. That's cool. Researchers just released World, an open-source Unreal Engine 5 simulator for training and testing LLM and VLM agents in realistic 3D environments. The platform supports RGB, depth, and segmentation sensors, along with navigation, vehicles, pedestrians, robots and procedural city generation. It's built with a Gym-like Python interface, it allows AI agents to learn physical and social reasoning in complex virtual worlds before real world deployment. We're moving from AI that only understands information to AI that can perceive, reason and act inside realistic simulated environments.

Wow. That's cool. Researchers just released World, an open-source Unreal Engine 5 simulator for training and testing LLM and VLM agents in realistic 3D environments. The platform supports RGB, depth, and segmentation sensors, along with navigation, vehicles, pedestrians, robots and procedural city generation. It's built with a Gym-like Python interface, it allows AI agents to learn physical and social reasoning in complex virtual worlds before real world deployment. We're moving from AI that only understands information to AI that can perceive, reason and act inside realistic simulated environments.

SciTech Era

43,522 görüntüleme • 3 gün önce

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The Humanoid Hub

68,453 görüntüleme • 4 ay önce

Today we are announcing Genie 3, a general purpose world model by Google DeepMind that can generate dynamic, interactive environments with a single text prompt. World models are AI that understand facets of the world (like Veo's knowledge of intuitive physics or Genie's mastery of new environments), and serve as a key stepping stone on the path to AGI. Genie 3 is our first world model to allow interaction in real-time, while also improving consistency and realism compared to Genie 2. Learn more ➡️

Today we are announcing Genie 3, a general purpose world model by Google DeepMind that can generate dynamic, interactive environments with a single text prompt. World models are AI that understand facets of the world (like Veo's knowledge of intuitive physics or Genie's mastery of new environments), and serve as a key stepping stone on the path to AGI. Genie 3 is our first world model to allow interaction in real-time, while also improving consistency and realism compared to Genie 2. Learn more ➡️

Google AI

102,408 görüntüleme • 10 ay önce