{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim","slug":"nvidia--physicalai-robotics-gr00t-x-embodiment-sim","name":"PhysicalAI-Robotics-GR00T-X-Embodiment-Sim","type":"dataset","url":"https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim","page_url":"https://unfragile.ai/nvidia--physicalai-robotics-gr00t-x-embodiment-sim","categories":["model-training"],"tags":["task_categories:robotics","license:cc-by-4.0","region:us","robotics"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_0","uri":"capability://data.processing.analysis.embodied.robot.trajectory.dataset.loading","name":"embodied-robot-trajectory-dataset-loading","description":"Loads and streams 334,635 pre-recorded robot manipulation trajectories from NVIDIA's GR00T-X embodied AI framework, organized by task category and robot morphology. Implements HuggingFace Datasets API for efficient memory-mapped access to multi-modal trajectory data (video frames, joint states, action sequences, language annotations) without requiring full dataset download. Supports streaming mode for training on machines with limited disk space.","intents":["Load robot manipulation trajectories for training embodied foundation models without downloading entire dataset","Stream multi-modal robot data (video + proprioceptive state) during model training with minimal memory overhead","Access task-specific trajectory subsets filtered by robot type, task category, or difficulty level"],"best_for":["robotics researchers training embodied foundation models like GR00T","teams building multi-robot manipulation systems requiring diverse trajectory data","ML engineers prototyping robot learning pipelines with limited storage"],"limitations":["Streaming mode requires stable internet connection; no offline-first caching mechanism","Dataset is optimized for NVIDIA robot hardware; direct transfer to non-NVIDIA platforms requires morphology adaptation","Trajectory data is pre-processed for GR00T architecture; custom preprocessing pipelines needed for other embodied models","No built-in temporal alignment across multi-robot trajectories; requires external synchronization for multi-agent scenarios"],"requires":["HuggingFace Datasets library 2.10+","Python 3.8+","Internet connection for streaming or 500GB+ disk space for full download","PyTorch or TensorFlow for trajectory tensor conversion"],"input_types":["HuggingFace dataset identifier string","task category filter (string)","robot morphology identifier (string)"],"output_types":["PyArrow Table with trajectory records","Video frames (numpy arrays, shape: [T, H, W, 3])","Joint state sequences (numpy arrays, shape: [T, num_joints])","Action sequences (numpy arrays, shape: [T, action_dim])","Language task descriptions (strings)"],"categories":["data-processing-analysis","robotics-datasets"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_1","uri":"capability://data.processing.analysis.multi.modal.trajectory.annotation.parsing","name":"multi-modal-trajectory-annotation-parsing","description":"Extracts and parses structured annotations from trajectory records including natural language task descriptions, robot morphology metadata, environment context, and action semantics. Implements a schema-based parser that maps raw trajectory fields to standardized embodied AI representations (state-action-reward tuples, task graphs, skill hierarchies). Supports filtering and grouping trajectories by semantic attributes without loading full video data.","intents":["Filter robot trajectories by task type, difficulty, or robot morphology without loading video frames","Extract natural language task descriptions to train language-conditioned robot policies","Build task-hierarchical datasets by grouping trajectories with shared semantic structure"],"best_for":["researchers training language-conditioned robot policies (e.g., VLA models)","teams building task-specific robot skill libraries from trajectory annotations","engineers filtering large trajectory datasets for targeted model training"],"limitations":["Annotation schema is fixed to GR00T-X task taxonomy; custom task ontologies require manual remapping","Natural language descriptions are template-generated, not human-written; may lack semantic diversity for language model training","No support for cross-task semantic similarity; requires external embedding models to find related trajectories","Metadata is sparse for environment context (lighting, object properties); limits sim-to-real transfer analysis"],"requires":["HuggingFace Datasets library 2.10+","Python 3.8+","JSON schema validation library (e.g., jsonschema)"],"input_types":["trajectory record (dict with 'task', 'robot_type', 'annotations' fields)","filter query (dict with semantic constraints)"],"output_types":["parsed annotation dict with standardized fields","filtered trajectory metadata (list of dicts)","task hierarchy graph (networkx Graph or adjacency list)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_2","uri":"capability://image.visual.video.trajectory.frame.extraction","name":"video-trajectory-frame-extraction","description":"Extracts and decodes video frames from trajectory records with configurable temporal sampling (every Nth frame, keyframes only, or full sequence). Implements efficient frame buffering and lazy loading to avoid memory exhaustion on large trajectory sequences. Supports multiple video codecs (H.264, VP9) and output formats (RGB, BGR, grayscale) with optional preprocessing (resizing, normalization) for model input compatibility.","intents":["Extract video frames from robot trajectories at custom temporal resolution for vision-based policy training","Decode trajectory videos with minimal memory footprint using lazy frame loading","Preprocess video frames to match model input specifications (resolution, normalization, color space)"],"best_for":["computer vision researchers training visual robot policies from trajectory video","teams building vision-language models for robot control","engineers optimizing data loading pipelines for GPU-bound training"],"limitations":["Video codec support depends on ffmpeg installation; H.265/HEVC may not be available on all systems","Frame extraction is sequential; no parallel decoding across trajectories, limiting throughput on multi-GPU setups","Temporal sampling is uniform; no adaptive keyframe selection for variable-speed trajectories","No built-in video quality assessment; corrupted or low-quality frames are not flagged"],"requires":["ffmpeg library (system-level installation)","OpenCV (cv2) 4.5+","NumPy 1.19+","Python 3.8+"],"input_types":["trajectory record with 'video' field (path or bytes)","sampling config dict (frame_skip, output_size, color_space)","frame indices (list of ints) or temporal range (tuple)"],"output_types":["video frames (numpy array, shape: [T, H, W, 3] or [T, H, W])","frame timestamps (numpy array, shape: [T])","metadata dict (codec, fps, resolution)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_3","uri":"capability://data.processing.analysis.proprioceptive.state.sequence.alignment","name":"proprioceptive-state-sequence-alignment","description":"Aligns joint state sequences (proprioceptive sensor readings) with video frames and action sequences using timestamp-based or frame-index synchronization. Handles variable-length trajectories and missing sensor data through interpolation or padding. Outputs aligned state-action-observation tuples suitable for imitation learning, with optional filtering for physically plausible state transitions (e.g., joint velocity limits).","intents":["Align robot joint states with video observations for training vision-proprioception fusion models","Create state-action-observation tuples from raw trajectory data for imitation learning","Filter trajectories for physically plausible state transitions to improve training data quality"],"best_for":["robotics researchers training multi-modal imitation learning models","teams building state-estimation pipelines that fuse vision and proprioception","engineers validating trajectory data quality before large-scale training"],"limitations":["Alignment assumes synchronized timestamps across modalities; clock drift or missing frames cause misalignment","Interpolation for missing sensor data assumes linear state evolution; non-linear dynamics (e.g., contact transitions) are not modeled","Physical plausibility filtering is based on hard joint limits; soft constraints (e.g., collision avoidance) are not checked","No support for asynchronous sensor streams (e.g., camera at 30Hz, proprioception at 100Hz); requires pre-synchronization"],"requires":["NumPy 1.19+","SciPy 1.5+ (for interpolation)","Python 3.8+","Robot URDF or kinematic model (for joint limit validation)"],"input_types":["trajectory record with 'joint_states', 'actions', 'video_frames' fields","alignment config dict (sync_method, interpolation_type, joint_limits)","optional robot kinematic model (URDF string or file path)"],"output_types":["aligned state-action-observation tuples (list of dicts)","alignment metadata (timestamps, interpolation flags)","quality metrics (alignment error, plausibility score)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_4","uri":"capability://data.processing.analysis.task.category.hierarchical.filtering","name":"task-category-hierarchical-filtering","description":"Organizes 334K trajectories into a task hierarchy (e.g., manipulation > grasping > pick-and-place) and enables filtering by task level, parent task, or task attributes. Implements a tree-based index structure for fast hierarchical queries without scanning all trajectories. Supports task similarity search to find related trajectories for curriculum learning or data augmentation.","intents":["Filter trajectories by task hierarchy level for curriculum learning (simple tasks before complex)","Find trajectories with similar task structure for data augmentation or transfer learning","Build task-specific datasets for training specialized robot skills"],"best_for":["researchers designing curriculum learning pipelines for embodied models","teams building modular robot skill libraries organized by task hierarchy","engineers analyzing task distribution in large trajectory datasets"],"limitations":["Task hierarchy is fixed to GR00T-X taxonomy; custom task ontologies require manual remapping","Task similarity is based on hierarchy structure only; semantic similarity (e.g., 'pick' vs 'grasp') requires external embeddings","No support for multi-task trajectories; each trajectory is assigned to a single task category","Hierarchy depth is limited (typically 3-4 levels); very fine-grained task distinctions are not supported"],"requires":["HuggingFace Datasets library 2.10+","Python 3.8+","Optional: networkx for hierarchy graph operations"],"input_types":["task category string (e.g., 'manipulation/grasping/pick')","filter depth (int, 1-4)","optional task attributes dict"],"output_types":["filtered trajectory indices (list of ints)","task hierarchy graph (networkx DiGraph or dict)","task statistics (count, distribution, depth)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_5","uri":"capability://data.processing.analysis.robot.morphology.specific.trajectory.selection","name":"robot-morphology-specific-trajectory-selection","description":"Filters trajectories by robot morphology (e.g., 7-DOF arm, mobile manipulator, humanoid) and enables morphology-aware data loading that adapts trajectory representations to target robot kinematics. Implements morphology metadata indexing for fast filtering and optional trajectory morphology conversion (e.g., remapping joint indices for different arm configurations).","intents":["Select trajectories for a specific robot morphology without loading irrelevant data","Adapt trajectories from one robot morphology to another for transfer learning","Analyze trajectory distribution across robot types in the dataset"],"best_for":["teams training morphology-specific robot policies","researchers studying sim-to-real transfer across different robot platforms","engineers building multi-robot systems with heterogeneous morphologies"],"limitations":["Morphology conversion assumes kinematically similar robots; conversion between very different morphologies (e.g., arm to humanoid) is not supported","Trajectory remapping requires explicit joint correspondence mapping; automatic correspondence discovery is not implemented","Morphology metadata is limited to kinematic structure; dynamic properties (mass, inertia) are not included","No support for morphology interpolation; trajectories are strictly filtered by exact morphology match"],"requires":["HuggingFace Datasets library 2.10+","Python 3.8+","Optional: robot URDF files for morphology validation"],"input_types":["target morphology identifier (string, e.g., 'ur10e_gripper')","optional morphology mapping dict (source_joints -> target_joints)","filter criteria (exact match or morphology family)"],"output_types":["filtered trajectory indices (list of ints)","morphology metadata (dict with DOF, joint types, end-effector info)","converted trajectories (optional, with remapped joint states)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_6","uri":"capability://data.processing.analysis.trajectory.batch.sampling.for.training","name":"trajectory-batch-sampling-for-training","description":"Implements efficient batch sampling strategies for training (random, sequential, stratified by task/morphology, curriculum-based) with support for weighted sampling to balance task distribution. Integrates with PyTorch DataLoader for distributed training across multiple GPUs/TPUs. Handles variable-length trajectories through padding, truncation, or dynamic batching.","intents":["Sample balanced batches of trajectories for training embodied models without manual data shuffling","Implement curriculum learning by progressively sampling harder tasks during training","Distribute trajectory sampling across multiple GPUs with minimal data duplication"],"best_for":["ML engineers training large-scale embodied foundation models","teams implementing curriculum learning for robot policy training","researchers running distributed training on multi-GPU clusters"],"limitations":["Variable-length trajectory handling requires padding or truncation, which may lose temporal information or introduce artifacts","Curriculum learning scheduling is manual; no automatic difficulty estimation","Stratified sampling assumes balanced task distribution; highly imbalanced datasets may require custom weighting","Distributed sampling assumes synchronous training; asynchronous or federated training is not supported"],"requires":["PyTorch 1.9+ or TensorFlow 2.5+","HuggingFace Datasets library 2.10+","Python 3.8+","Optional: Distributed training framework (PyTorch DDP, Horovod)"],"input_types":["sampling strategy (string: 'random', 'sequential', 'stratified', 'curriculum')","batch size (int)","optional task weights dict or curriculum schedule","optional trajectory length constraints"],"output_types":["batched trajectories (dict with 'observations', 'actions', 'states' keys)","batch metadata (task labels, morphology, trajectory indices)","sampling statistics (task distribution, curriculum progress)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_7","uri":"capability://data.processing.analysis.trajectory.quality.assessment.and.filtering","name":"trajectory-quality-assessment-and-filtering","description":"Analyzes trajectories for quality metrics including action smoothness, state plausibility, video frame quality, and task completion indicators. Implements automated filtering to remove low-quality trajectories (e.g., with jerky motions, sensor noise, or incomplete tasks) without manual inspection. Outputs quality scores and filtering recommendations for dataset curation.","intents":["Identify and remove low-quality trajectories that could degrade model training","Assess video quality (blur, noise, occlusion) to filter out problematic frames","Validate that trajectories represent successful task completion"],"best_for":["data engineers curating large trajectory datasets for model training","researchers analyzing dataset quality and its impact on model performance","teams building robust data pipelines with automated quality gates"],"limitations":["Quality metrics are heuristic-based; no ground truth for what constitutes 'good' trajectory quality","Action smoothness assumes continuous control; discrete or hybrid actions may be incorrectly flagged as jerky","Video quality assessment is basic (blur detection, noise estimation); advanced issues (occlusion, lighting changes) are not detected","Task completion validation requires task-specific success criteria; generic completion detection is not reliable"],"requires":["NumPy 1.19+","SciPy 1.5+ (for signal processing)","OpenCV 4.5+ (for video quality assessment)","Python 3.8+"],"input_types":["trajectory record with 'actions', 'states', 'video_frames' fields","quality config dict (smoothness_threshold, noise_threshold, etc.)","optional task success criteria dict"],"output_types":["quality scores dict (action_smoothness, state_plausibility, video_quality, task_completion)","filtering recommendation (bool: keep/discard)","detailed quality report (list of issues found)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nvidia--physicalai-robotics-gr00t-x-embodiment-sim__cap_8","uri":"capability://data.processing.analysis.trajectory.augmentation.and.synthesis","name":"trajectory-augmentation-and-synthesis","description":"Generates synthetic trajectory variations through action perturbation, state interpolation, and video augmentation (rotation, scaling, color jittering). Implements physics-aware augmentation that respects joint limits and collision constraints. Supports trajectory mixing (blending two trajectories) for data augmentation without manual trajectory recording.","intents":["Augment trajectory dataset to increase diversity without additional robot data collection","Generate trajectory variations for robustness training (e.g., handling camera angle changes)","Create synthetic intermediate trajectories through interpolation for smoother learning"],"best_for":["teams with limited trajectory data seeking to maximize dataset diversity","researchers training robust embodied models that handle visual and action variations","engineers implementing data augmentation pipelines for imitation learning"],"limitations":["Physics-aware augmentation requires robot kinematic model; augmentation is limited without URDF","Action perturbation assumes continuous control; discrete actions cannot be meaningfully perturbed","Trajectory mixing assumes similar task structure; mixing trajectories from different tasks may produce invalid sequences","Video augmentation is standard (rotation, scaling, color); domain-specific augmentations (e.g., lighting changes, object appearance) are not supported"],"requires":["NumPy 1.19+","SciPy 1.5+ (for interpolation)","Pillow 8.0+ or OpenCV 4.5+ (for image augmentation)","Python 3.8+","Optional: robot URDF for physics-aware augmentation"],"input_types":["trajectory record with 'actions', 'states', 'video_frames' fields","augmentation config dict (perturbation_scale, interpolation_method, video_transforms)","optional robot kinematic model (URDF)"],"output_types":["augmented trajectory record (same structure as input)","augmentation metadata (perturbation magnitude, interpolation ratio)","batch of augmented trajectories (for multi-augmentation)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["HuggingFace Datasets library 2.10+","Python 3.8+","Internet connection for streaming or 500GB+ disk space for full download","PyTorch or TensorFlow for trajectory tensor conversion","JSON schema validation library (e.g., jsonschema)","ffmpeg library (system-level installation)","OpenCV (cv2) 4.5+","NumPy 1.19+","SciPy 1.5+ (for interpolation)","Robot URDF or kinematic model (for joint limit validation)"],"failure_modes":["Streaming mode requires stable internet connection; no offline-first caching mechanism","Dataset is optimized for NVIDIA robot hardware; direct transfer to non-NVIDIA platforms requires morphology adaptation","Trajectory data is pre-processed for GR00T architecture; custom preprocessing pipelines needed for other embodied models","No built-in temporal alignment across multi-robot trajectories; requires external synchronization for multi-agent scenarios","Annotation schema is fixed to GR00T-X task taxonomy; custom task ontologies require manual remapping","Natural language descriptions are template-generated, not human-written; may lack semantic diversity for language model training","No support for cross-task semantic similarity; requires external embedding models to find related trajectories","Metadata is sparse for environment context (lighting, object properties); limits sim-to-real transfer analysis","Video codec support depends on ffmpeg installation; H.265/HEVC may not be available on all systems","Frame extraction is sequential; no parallel decoding across trajectories, limiting throughput on multi-GPU setups","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.42,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nvidia--physicalai-robotics-gr00t-x-embodiment-sim","compare_url":"https://unfragile.ai/compare?artifact=nvidia--physicalai-robotics-gr00t-x-embodiment-sim"}},"signature":"ep6n9z+73fHGIFmIJUXzwvkuDYfx6Q6vJGQMpHFSWuweV8ojNycj8NjwPCoEfuuYotOpSrP/KX3090+nwiMFBw==","signedAt":"2026-06-20T08:27:14.205Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nvidia--physicalai-robotics-gr00t-x-embodiment-sim","artifact":"https://unfragile.ai/nvidia--physicalai-robotics-gr00t-x-embodiment-sim","verify":"https://unfragile.ai/api/v1/verify?slug=nvidia--physicalai-robotics-gr00t-x-embodiment-sim","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}