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1. Overview For a robot to move autonomously in the real world and perform tasks, it requires a massive amount of training data. However, collecting such data directly in the real world is expensive and time-consuming. This is why NVIDIA presents the full pipeline of the GR00T ecosystem, which covers automatic generation, augmentation, training, and inference of robot-learning data. This document summarizes how GR00T-Dreams and GR00T-Mimic differ, and how each generates training data that real robots can use. 2. GR00T Ecosystem Architecture GR00T is not a single model but an ecosystem of multiple components that together form the full robot-learning pipeline. GR00T Ecosystem Components GR00T-Dreams: Generates new scenarios and new action data — starts from minimal inputs such as teleop, images, or language and produces action videos GR00T-Mimic: Augments and diversifies existing demonstrations or robot data Cosmos: Predict / Reason / Transfer — a collection of models that handle action prediction, 3D understanding, data transformation, and augmentation Isaac Lab / GR00T-Omni: A physics-based robot simulation environment — reinforcement learning (policy training) occurs here GR00T-RT / RFM (Robot Foundation Model): The stage where trained policies are executed on real robots In short, Dreams and Mimic handle data generation, Isaac Lab handles learning, and RFM handles execution. 3. GR00T-Mimic – Blueprint for Expanding Existing Data Concept Mimic is literally a pipeline that imitates, transforms, and expands existing demonstration data. Its inputs include: Human teleoperation data Existing robot manipulation trajectories Data recorded from Isaac Sim Human/robot expert demonstrations By altering environments, lighting, object placement, motion speed, or object states, Mimic generates large volumes of new training data. Features Input: existing trajectories Output: thousands to tens of thousands of trajectory variations for the same task Goal: improve robot proficiency on specific tasks Technology: augmentation via Isaac Sim, Isaac Lab, Cosmos-Transfer Summary Mimic is optimized for “mastering known tasks.” 4. GR00T-Dreams – Creating New Scenarios Concept Dreams creates entirely new task scenarios from scratch. It can start from extremely minimal input, such as: A single image A text description A short teleop demonstration Dreams generates human action videos from these minimal inputs, and Cosmos analyzes those videos to convert them into robot-learnable trajectories. Key Features Input: minimal information (image / text / short demo) Output: completely new task trajectories Goal: enhance robot generalization Technology: DreamGen, Cosmos Predict/Reason, pose reconstruction 5. Dreams Is Not Just a Video Generator Many people think “Dreams is simply a video generator.” However, the real value lies beyond the video. After generating video, Dreams includes the full pipeline to convert it into structured robot-training data. In other words, Dreams performs the full transformation: video → 3D pose → robot trajectory → physics-based torque. 6. GR00T-Dreams Pipeline: Video → Robot Training Data This is where Dreams’ technical strength is best revealed. ① DreamGen: Action Video Generation Input: text, images, short demonstrations Output: natural human action videos At this stage, no joint data exists yet ② Cosmos Predict/Reason: 3D Pose Reconstruction From each video frame, Cosmos extracts: 3D skeleton Hand orientation Body segment trajectories Object–hand interactions Pixel-based video is transformed into structured 3D motion data. ③ Retargeting: Human Motion → Robot Joint Space Human 3D motion is converted into the robot’s joint space (q, qdot), respecting: joint limits balance constraints reachable workspace robot kinematics At this stage, Action Tokens (robot action representations) are generated. ④ Inverse Dynamics: Reconstructing Physical Quantities To execute motions in the real world, the robot needs physics values such as: torque contact forces momentum foot placement forces As a result, Dreams generates complete trajectory data ready for immediate policy training. 7. Summary Comparison: Mimic vs Dreams 8. Conclusion Dreams and Mimic serve different purposes and use different technologies, yet both play critical roles in generating robot-training data. Mimic: enhances proficiency in known tasks Dreams: creates new tasks and expands generalization Cosmos: core model suite for transforming Dreams/Mimic data Isaac Lab: environment for robot policy training RFM: executes trained policies on real robots 9. Related Links Training Humanoid Robots With Isaac GR00T-Dreams https://www.youtube.com/watch?v=pMWL1MEI-gE Teaching Robots New Tasks With GR00T-Dreams https://www.youtube.com/watch?v=QHKH4iYYwJs GR00T: NVIDIA Humanoid Robotics Foundation Model https://www.youtube.com/watch?v=ZSxYgW-zHiU Isaac GR00T-Mimic: Isaac Lab Office Hour https://www.youtube.com/watch?v=r24CiGLYFQo
Dec 4, 2025
PINNs(Physics-Informed Neural Networks) at a Glance PINNs (Physics-Informed Neural Networks) are analytical neural networks embedded with physical constraints, the World Model is a representation model that compresses and predicts the dynamics of an environment, and the Robot Foundation Model is a large-scale action model integrating perception and behavior. 1. Overall Relationship and Data Flow ┌───────────────────────────────────────────────┐ │ Physics Layer (PINNs) │ │───────────────────────────────────────────────│ │ • Embedding physical equations (PDEs, BCs) │ │ • Learning continuous solution functions │ │ • Generating physics-consistent synthetic data│ └──────────────┬────────────────────────────────┘ │ Providing physically consistent data ▼ ┌───────────────────────────────────────────────────┐ │ Cognitive Layer (World Foundation Model) │ │───────────────────────────────────────────────────│ │ • Latent representation of environmental dynamics │ │ • Counterfactual prediction / imagined worlds │ │ • Learning world models based on physics │ └──────────────┬────────────────────────────────────┘ │ Providing simulated environments ▼ ┌─────────────────────────────────────────────────┐ │ Behavioral Layer (Robot Foundation Model) │ │─────────────────────────────────────────────────│ │ • Integrated learning of vision/language/action │ │ • Learning generalized behavior policies │ │ • Transfer of skills from simulation to reality │ └──────────────┬──────────────────────────────────┘ │ Real-world feedback (sensors, actions) ▼ ┌───────────────────────────────────────────────┐ │ Feedback Loop (Self-Consistency) │ │───────────────────────────────────────────────│ │ • RFM → WFM: Refining prediction accuracy │ │ • WFM → PINNs: Adjusting physical boundaries │ │ • PINNs → RFM: Ensuring physical stability │ └───────────────────────────────────────────────┘ 1. PINNs → World Model PINNs generate high-quality synthetic data that reflect physical laws. This data is used by the world model to learn environmental dynamics and physical interactions. Example: Simulating trajectories when a robot pushes an object or reactions during collision to provide training data for the world model. 2. World Model → Robot Foundation Model The world model acts as an environment simulator and generates large-scale synthetic data that can be used to train the Robot Foundation Model. Through this, the Robot Foundation Model learns generalized control policies for various tasks and environments. Example: Using simulation data from the world model, the robot learns how to manipulate objects or avoid obstacles. 3. Overall Flow PINNs: Generate synthetic data with guaranteed physical accuracy. World Model: Uses PINNs data to simulate environments and generates data required for robot training. Robot Foundation Model: Utilizes simulation data from the world model to learn generalized and scalable robot control capabilities. 2. PINNs (Physics-Informed Neural Networks) PINNs are neural networks that embed physical laws directly into the loss function to learn functions that satisfy those equations. Traditional numerical methods (FEM, CFD, etc.) divide space into grid points and repeatedly compute values at each node, whereas PINNs approximate the entire space with a single continuous function. In other words, instead of memorizing values at specific points, the neural network internalizes the form of solutions that satisfy physical equations. The key is physics-based learning. Because PINNs use PDEs and boundary conditions directly as training objectives, they can be trained even with limited data, offer strong generalization to new boundaries or conditions, reduce computational costs of physical simulations, and generate large-scale physically consistent synthetic data. Key Features: Physical equations (e.g., Navier–Stokes, heat transfer) are embedded in the loss function for training. Can be trained using analytical or experimental data. Ensures physically consistent outputs. Provides quality standards for synthetic data generation. Maintains fundamental physical consistency in simulations. Direct Objective: Learning solution functions that satisfy physical equations The most explicit objective of PINNs is: “To approximate solutions of differential equations not through numerical data, but within the structure of the neural network itself.” That is, rather than numerically solving PDEs like FEM or CFD, PINNs learn a function that inherently satisfies those equations. This approach is fundamentally about representation, not repeated computation. Once trained, a PINN can instantly output a continuous solution at any coordinate, even outside of grid points. Deeper Objective: Continuous integration of multi-physics Real-world phenomena almost always occur at the interfaces of multiple physical domains: Fluid impacting a solid → interaction of pressure and stress Heat transfer → temperature, deformation, and flow are coupled Electric current → electromagnetic, thermal, and mechanical stress interactions Traditional numerical solvers treat domains like fluid, structure, and thermal separately, exchanging values at boundaries — causing discontinuities and instability. PINNs instead learn all relevant physical equations within a single neural function, jointly approximating fluid PDEs, solid PDEs, and thermal equations in one continuous solution space. Representative Models: NVIDIA Physics NeMo (2023) – GPU-accelerated PINN framework for multi-physics. DeepXDE (Lu et al., 2021) – Open-source PINNs library for solving PDEs like Navier–Stokes, heat equations. https://github.com/lululxvi/deepxde mathLab/PINA (Haghighat & Juanes, 2025) – PINNs library built on PyTorch Lightning and PyTorch Geometric for scientific machine learning. https://github.com/mathLab/PINA 3. Extended Architecture of PINNs — NVIDIA PhysicsNeMo + Omniverse PhysicsNeMo An open-source physics-ML framework that enables large-scale training and inference for PINNs, FNOs, and other SciML models (Python-based, open-source). Focused on transforming large physics models into real-time or near real-time predictors. (Reference: NVIDIA PhysicsNeMo) PINNs Documentation & Tutorials Official documentation provides guidance on how to incorporate PDEs into loss functions and implement physics-informed learning (includes legacy Modulus documentation as well). (Reference: PINNs in PhysicsNeMo Sym) CFD / Operator Learning (FNO) Integration PhysicsNeMo supports Fourier Neural Operator (FNO) implementations to simplify the creation of surrogate models for large-scale fluid or thermal simulations. (Reference: Transforming CFD Simulations with ML Using NVIDIA PhysicsNeMo) Omniverse & Cosmos Integration Points Cosmos (WFM): A World Foundation Model platform for Physical AI (robots, autonomous driving, etc.). Supports synthetic data generation, guardrails, and accelerated pipelines (announced/updated in 2025). Together with PhysicsNeMo-based physics predictors, Cosmos can be used to construct a physically consistent world model. (Reference: NVIDIA Launches Cosmos World Foundation Model Platform) Omniverse Robotics Libraries New robotics libraries/tools were released to accelerate workflows from simulation → data generation → training, in conjunction with Cosmos. (Reference: Developers Build Fast and Reliable Robot Simulations with NVIDIA Omniverse Libraries) 4. Industrial Applications (Applications & Case Studies) A. Product/Design Acceleration (Engineering) Product Development Acceleration: Solving forward/inverse problems using PINN-based physics-ML shortens design and validation cycles (documented in NVIDIA’s official blog). (Reference: Accelerating Product Development with Physics-Informed Neural Networks and NVIDIA PhysicsNeMo) CFD Replacement/Complement: PhysicsNeMo’s FNO-based surrogate models accelerate iterative design/optimization processes. (Reference: Transforming CFD Simulations with ML Using NVIDIA PhysicsNeMo) B. Digital Twin & Simulation Operations Using documented PINN techniques, physics-consistent predictors can be embedded into digital twins on Omniverse for real-time or near real-time responses. (Reference: Physics Informed Neural Networks in Modulus) C. Robotics / Autonomous Systems Data Generation With Cosmos WFM, various environments and scenarios are synthesized, while PhysicsNeMo/PINNs provide physics-based reactions (collision, flow, heat, etc.) → improving training data quality for robot behavior models. (Reference: NVIDIA Announces Major Release of Cosmos World Foundation Models and Physical AI Data Tools) NVIDIA’s Omniverse + Cosmos robotics workflow update formalizes pipelines for simulation, synthetic data generation, and training. D. Academic/Industrial Hybrid Cases PINNs have been used to solve optimization and pathfinding problems, where physical constraints helped discover narrow or unstable solutions that traditional RL/GA algorithms struggle with. (Reference: Solving real-world optimization tasks using physics-informed neural computing)
Nov 4, 2025
1. Overall Overview of Sensors https://docs.isaacsim.omniverse.nvidia.com/latest/sensors/index.html Isaac Sim’s sensor system is divided into six categories and supports simulation of various sensors that are physics-based, RTX-based, camera-based, and applicable to real robots. Each category is used for the following purposes. 2. Camera Sensors https://docs.isaacsim.omniverse.nvidia.com/latest/sensors/isaacsim_sensors_camera.html The basic RGB/Depth cameras used in Isaac Sim can be simulated similar to real lenses and support various annotator outputs. What are Annotators? In Isaac Sim, an annotator automatically generates additional ground-truth data streams beyond the images produced by a sensor. This data is extremely useful for machine learning training, robot perception, and validation. Major Annotator Types and Descriptions: In Isaac Sim, this data can be output as .png, .json, .npy, ROS messages, etc., and can be controlled via Python API or Action Graph. Supported Features: Configure focal length, FOV, resolution, sensor size Apply lens distortion models (pinhole, fisheye, etc.) Integration with render products for rendering Annotator support: RGB, Depth, Normals, Motion Vectors, Instance Segmentation, etc. Creation and control via Python or GUI Example Uses: Simulation for object recognition data collection Synthetic data generation for deep-learning-based vision training RGB-D data output for ROS/SLAM integration 3. RTX Sensors https://docs.isaacsim.omniverse.nvidia.com/latest/sensors/isaacsim_sensors_rtx.html A family of high-precision sensors for distance/velocity detection using NVIDIA RTX acceleration. It consists of lidar, radar, and visual annotators. Subcomponents: RTX Lidar Sensor: Outputs point clouds; adjustable rotation angle, FOV, and ray density RTX Radar Sensor: Extracts distance + velocity (simulates Doppler effect) RTX Sensor Annotators: Output properties for visualization Visual/Non-Visual Materials: Materials for sensor response Example Uses: Autonomous vehicles, AMRs, and robot ranging Comparing detection capabilities across sensor types Visual Materials (Sensor Materials for Visual Response) Simulate the visual properties of object surfaces that RTX sensors detect (e.g., reflectance, absorption). These affect both rendering and sensor response; a total of 21 fixed material types are provided. Non-Visual Sensor Material Properties Do not affect rendering, but control detectability and reflectance strength for RTX sensors. This allows specific objects to be detected or ignored in simulation. Purposes: Improve accuracy of sensor tests Make certain objects detectable or hidden Keep visual material intact while altering only sensor response 4. Physics-Based Sensors https://docs.isaacsim.omniverse.nvidia.com/latest/sensors/isaacsim_sensors_physics.html Sensors implemented based on interactions in the physics engine (PhysX) to sense internal robot states or external contacts. Supported Sensors: Articulation Joint Sensor: Full joint state sensing (position/velocity/effort) Contact Sensor: Ground contact, collision detection Effort Sensor: Measures joint torque only IMU Sensor: Inertial data (acceleration, gyro) Proximity Sensor: Simple detection of whether an object exists within a certain range (True/False) Example Uses: Evaluating robot walking stability Force/torque analysis for manipulators Landing detection, fall detection 5. PhysX SDK Sensors https://docs.isaacsim.omniverse.nvidia.com/latest/sensors/isaacsim_sensors_physx.html Lightweight distance sensors using the PhysX SDK’s raycast functionality. Supported Items: Generic Sensor: Custom ray-based sensing PhysX Lidar: Fixed lidar simulation (low-cost version) Lightbeam Sensor: One-way detector similar to a laser Example Uses: Simple distance-based obstacle detection Environment change detection Low-compute-cost sensor simulation 6. Camera and Depth Sensors (USD Assets) https://docs.isaacsim.omniverse.nvidia.com/latest/assets/usd_assets_camera_depth_sensors.html A collection of USD-based camera and depth sensor assets modeled after frequently used real sensors. Included Sensors: Example Uses: Sim-to-Real consistency testing with specific real sensors Replicating real sensor positions/fields of view 7. Non-Visual Sensors (USD Assets) https://docs.isaacsim.omniverse.nvidia.com/latest/assets/usd_assets_nonvisual_sensors.html A collection of assets for digital twins of non-visual sensors (IMU, force, contact, etc.). Asset Contents: Example Uses: Extracting internal robot physical quantities (forces, acceleration) Motion estimation, control feedback system development
Nov 4, 2025
Practice Guide: Loading GR00T-Mimic Teleoperation Data + Annotation Key Message How to define subtask end points for each robot operation using teleoperation data and generate training data. Summary Step 1: Load Teleoperation Data Load robot demonstration data (e.g., GR-1) inside Isaac Sim Initial state: the robot remains in Idle mode Using the loaded demonstration data, the robot can begin task execution Step 2: Play Trajectory & Mark Subtask End Points N key: Start playback of the teleoperation trajectory B key: Pause playback S key: Mark the end point of the current subtask Example: Mark when the right arm completes the first task (Idle → Grasp) Example: Mark when the left arm completes another task (Grasp → Place) The end of the final task is implicitly defined (no need to manually mark) Step 3: Example of Teleoperation Right Arm Task: Initial state: Idle Task: Mark the end point of the first subtask (e.g., grasping an object) Left Arm Task: Initial state: Idle Task: Mark the end after grasping the beaker The final step is automatically considered as the end of the subtask Step 4: Automated Data Generation Based on the marked end points, GR00T-Mimic converts data into a training-ready format High precision is not required; rough marking of end points is sufficient GR00T-Mimic uses interpolation to generate smooth and robust training trajectories Isaac Lab Teleoperation and Imitation Learning Reference Link https://isaac-sim.github.io/IsaacLab/main/source/overview/teleop_imitation.html Practice Guide: GR00T-Mimic Data Generation & Playback Key Message How to define subtask end points using teleoperation data and generate training data. Summary Step 1: Run Data Generation Execute the command below in the terminal: This allows data generation in 4 parallel environments You can change the number of parallel environments by modifying -num_envs Step 2: Motion Simulation During data generation, random action noise is added in addition to the original motion You may see the robot arm slightly shaking The trained model learns robustness under such variations and noise If needed, you can adjust the noise level using parameters such as -noise_level Step 3: Using Headless Mode For large-scale data generation, you can run without full Isaac Sim visualization (headless mode) Improves performance python run_data_generation.py --num_envs 100 --headless Step 4: Replay Generated Data You can visualize the generated data to verify quality Run the replay script: python replay_data.py --dataset_dir path/to/dataset You can replay both the original robot motion and the motion with action noise You can specify the number of parallel environments (-num_envs) -generation_num_trials 1000 Total number of trials (samples) to generate For example: generates 1000 datasets -num_env 4 Number of parallel environments used during data generation Generating 1000 datasets in a single environment takes a long time With -num_env 4, four datasets can be generated simultaneously → up to 4× faster Practice Guide: GR00T-Mimic Trained Policy Key Message How to define subtask end points using teleoperation data and generate training data. Summary Step 1: Train the Robot Policy Using Generated Data After collecting teleoperation demonstration data, Train the robot using the generated dataset (e.g., 1000 demonstrations) The trained policy uses an LSTM (Long Short-Term Memory) model About the LSTM Policy A type of RNN well-suited for learning time-series data In robot control, it is useful for learning sequential actions such as: grasp → move → place → return It captures temporal dependencies between consecutive actions Step 2: Performance of the Trained Policy The trained policy becomes robust not only to original motion but also to randomized data After training, the robot shows little to no action noise (trembling) that appeared during data generation Step 3: Controlling Randomization Range In the training environment, the position of objects can be randomized Even with the same original demonstration, you can adjust the randomization range Example: increase the object position randomization compared to the original demo If the randomization range is too large: Training performance may degrade due to mismatch between original and generated data Therefore, randomization must remain within a reasonable boundary
Nov 4, 2025
