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Simulating and training robots requires a physics-first approach. 🤖 With Newton, the open-source physics simulation engine co-developed by Disney Research, Walt Disney Imagineering, Google DeepMind and NVIDIA, Disney’s BDX Droid can move through shifting sand and gravel with exceptional precision. Watch to see how Newton is playing a key...

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NVIDIA Robotics

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From rugged terrain to soft fabrics, this droid handles it all. 🤖 With Newton, the open-source physics simulation engine co-developed by Google DeepMind and Disney Research, Walt Disney Imagineering, robots can move through shifting sand or gravel and manipulate delicate materials with greater precision than ever before. Learn how Newton is advancing robot learning and shaping the next generation of robotic systems. Watch it in action with Disney’s BDX Droid: #NVIDIAGTC
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From rugged terrain to soft fabrics, this droid handles it all. 🤖 With Newton, the open-source physics simulation engine co-developed by Google DeepMind and Disney Research, Walt Disney Imagineering, robots can move through shifting sand or gravel and manipulate delicate materials with greater precision than ever before. Learn how Newton is advancing robot learning and shaping the next generation of robotic systems. Watch it in action with Disney’s BDX Droid: #NVIDIAGTC

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Jensen just announced Newton, an open-source physics engine for robotics simulation developed by NVIDIA, Google DeepMind, and Disney Research. ⦿ Built on NVIDIA Warp, it offers GPU-accelerated performance and compatibility with frameworks like MuJoCo and Isaac Lab. ⦿ Features differentiable physics, extensibility for multiphysics simulations, and integration with OpenUSD for flexible data modeling. ⦿ Newton addresses the sim-to-real gap by creating realistic virtual environments for robot training and testing. ⦿ Disney Research is using Newton to power next-gen entertainment robots, including Star Wars-inspired BDX droids.
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Jensen just announced Newton, an open-source physics engine for robotics simulation developed by NVIDIA, Google DeepMind, and Disney Research. ⦿ Built on NVIDIA Warp, it offers GPU-accelerated performance and compatibility with frameworks like MuJoCo and Isaac Lab. ⦿ Features differentiable physics, extensibility for multiphysics simulations, and integration with OpenUSD for flexible data modeling. ⦿ Newton addresses the sim-to-real gap by creating realistic virtual environments for robot training and testing. ⦿ Disney Research is using Newton to power next-gen entertainment robots, including Star Wars-inspired BDX droids.

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The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥
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The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥

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Have you seen Walt Disney Imagineering's BDX Droid, Grek? Watch Grek in action on stage and see how Disney Research, @GoogleDeepmind and @NVIDIAOmniverse work together to bring next-generation robots to life.
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Robots in theme parks! 🎢 Theme parks are starting to use robots to improve entertainment. Universal Studios Hollywood is turning Boston Dynamics robot dogs into baby dragons based on 'How to Train Your Dragon' movie. The robots walk, move, and act like real creatures, letting guests interact with them up close. This makes the park experience more fun and memorable. It also shows how robotics can be used in creative, hands-on ways. Expect to see more of this in future attractions. Enthusiasm for entertainment robots among humans is huge. This is proven, for example, by the robots created by Walt Disney Imagineering.
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Robots in theme parks! 🎢 Theme parks are starting to use robots to improve entertainment. Universal Studios Hollywood is turning Boston Dynamics robot dogs into baby dragons based on 'How to Train Your Dragon' movie. The robots walk, move, and act like real creatures, letting guests interact with them up close. This makes the park experience more fun and memorable. It also shows how robotics can be used in creative, hands-on ways. Expect to see more of this in future attractions. Enthusiasm for entertainment robots among humans is huge. This is proven, for example, by the robots created by Walt Disney Imagineering.

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Nvidia showcases Blue, a cute little robot powered by the Newton physics engine. This was the „wow“-moment for me. Robots are coming. This time for real. And I am all in for it!
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Nvidia showcases Blue, a cute little robot powered by the Newton physics engine. This was the „wow“-moment for me. Robots are coming. This time for real. And I am all in for it!

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NVIDIA showcases Blue, a cute little Star Wars robot with personality developed by Disney and Google DeepMind
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NVIDIA showcases Blue, a cute little Star Wars robot with personality developed by Disney and Google DeepMind

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World Simulator, reimagined — now alive with humans, robots, and their vibrant society unfolding in 3D real-world geospatial scenes across the globe! 🚀 One day soon, humans and robots will co-exist in the same world. To prepare, we must address: 1️⃣ How can robots cooperate or compete intelligently? 2️⃣ How do humans build social bonds and communities? 3️⃣ How can both co-exist in an open, dynamic world? Announcing Virtual Community Project — a social-physical world simulator, where human characters and robotic agents can interact, grow, and co-evolve within open-world societies, stretching from London to New York, and beyond! Key features include: ✅ Unified multi-agent physics simulations for rich social + physical interactions of humans and robots ✅ Massive auto-generated 3D scenes grounded with the rea-world geospatial data ✅ Agent communities populated by robots and LLM-driven human characters with rich appearances, personalities, and social ties. 🌍 Enter our Virtual Community, an open world to study embodied AI at scale— one social-physical world model at a time! 🔗 Project: 💻 Code: Paper: 1/n
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World Simulator, reimagined — now alive with humans, robots, and their vibrant society unfolding in 3D real-world geospatial scenes across the globe! 🚀 One day soon, humans and robots will co-exist in the same world. To prepare, we must address: 1️⃣ How can robots cooperate or compete intelligently? 2️⃣ How do humans build social bonds and communities? 3️⃣ How can both co-exist in an open, dynamic world? Announcing Virtual Community Project — a social-physical world simulator, where human characters and robotic agents can interact, grow, and co-evolve within open-world societies, stretching from London to New York, and beyond! Key features include: ✅ Unified multi-agent physics simulations for rich social + physical interactions of humans and robots ✅ Massive auto-generated 3D scenes grounded with the rea-world geospatial data ✅ Agent communities populated by robots and LLM-driven human characters with rich appearances, personalities, and social ties. 🌍 Enter our Virtual Community, an open world to study embodied AI at scale— one social-physical world model at a time! 🔗 Project: 💻 Code: Paper: 1/n

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NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:
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NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:

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A new paper by Disney Research introduces a 2-stage technique for motion tracking in physics-based character animation, using a pretrained latent space and RL to control diverse and unseen motions on both virtual and real-world humanoid robots. Paper:
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A new paper by Disney Research introduces a 2-stage technique for motion tracking in physics-based character animation, using a pretrained latent space and RL to control diverse and unseen motions on both virtual and real-world humanoid robots. Paper:

The Humanoid Hub

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MEET BLUE: NVIDIA’S REAL-LIFE WALL-E Blue is an AI robot that moves, reacts, and talks like something straight out of a Pixar film. Blue runs on 2 NVIDIA computers and uses a physics engine for ultra-realistic tactile feedback, rigid and soft body simulation, all in real time. This isn’t a concept, this is how NVIDIA plans to train robots for the real world. And yes, it casually stole the show. Source: entrepreneurialogy

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Out now with PSP 1.4 (See bi0) Hook by regions: An advanced hooking method that can interact with physics. It allows you to move a physics mesh in real-time and animate your actions. #blender #b3d #3DCG #3danimated #3dart
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Out now with PSP 1.4 (See bi0) Hook by regions: An advanced hooking method that can interact with physics. It allows you to move a physics mesh in real-time and animate your actions. #blender #b3d #3DCG #3danimated #3dart

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Disney is doing what only Disney can: spending massive R&D budgets to make robots "lovable." 💸🤖 By solving the physics of character animation with Olaf, they are creating the blueprint for mainstream acceptance. Here is how startups like Cartwheel, Sunday & Tangible are following that lead to get robots into our homes. 👇

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ELON: TESLA’S ROBOTS ARE MOVING FROM SIMULATIONS TO REALITY “For the robot, we’re going to need to build a lot of them and put them in an Optimus Academy so they can do self-play in reality and test different tasks. Tesla has quite a good, physics accurate, reality-generator, and we’re doing that for the robots. So you can do millions of robots in a simulated world, and then 10k in the real world to close the simulation to reality gap. Grok would orchestrate the behavior of the Optimus robots.” Source: Cheeky Pint Podcast, Elon Musk

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19,289 Aufrufe • vor 4 Monaten

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🤖The "Physical Turing Test": Can robots clean rooms & prepare dinners indistinguishably from humans? @Drjimfan explains how @Nvidia overcomes robotics' data bottleneck by simulating thousands of environments with varied physics at AI Ascent.

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Roboticists from Robotic Systems Lab and NVIDIA Embedded are teaching four-legged robots climb and jump. After training in simulation, the robots can autonomously decide how to scramble over and under obstacles, which will help them do dangerous jobs so that humans don't have to.
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Roboticists from Robotic Systems Lab and NVIDIA Embedded are teaching four-legged robots climb and jump. After training in simulation, the robots can autonomously decide how to scramble over and under obstacles, which will help them do dangerous jobs so that humans don't have to.

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2,317,927 Aufrufe • vor 2 Jahren

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n
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Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

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