{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","slug":"rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","name":"RT-1: Robotics Transformer for Real-World Control at Scale (RT-1)","type":"model","url":"https://arxiv.org/abs/2212.06817","page_url":"https://unfragile.ai/rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_0","uri":"capability://automation.workflow.vision.language.conditioned.robotic.manipulation.control","name":"vision-language-conditioned robotic manipulation control","description":"RT-1 uses a transformer-based architecture that processes both natural language instructions and visual observations (RGB images from robot cameras) to generate low-level motor control commands. The model encodes language tokens and image patches through separate embedding streams, fuses them via cross-attention mechanisms, and outputs discretized action tokens representing joint angles, gripper positions, and movement magnitudes. This enables a single unified model to control diverse robotic arms across different morphologies by learning shared representations of manipulation intent.","intents":["Train a single robot control model that understands both language commands and visual context to perform manipulation tasks","Enable robots to generalize manipulation skills across different object types and scene configurations without task-specific retraining","Deploy a language-conditioned policy that can adapt to new instructions at inference time without fine-tuning"],"best_for":["robotics research teams building scalable manipulation systems","companies deploying multi-robot fleets with diverse hardware configurations","developers creating language-guided robotic automation for industrial or service tasks"],"limitations":["Requires large-scale diverse robot trajectory data (RT-1 trained on 130k+ real-world demonstrations) — not practical for single-robot setups","Discretized action space limits fine-grained control precision; continuous action variants require separate training","Generalization to significantly different robot morphologies (e.g., humanoid vs. industrial arm) not demonstrated; primarily validated on similar arm configurations","Inference latency ~200-500ms per action step depending on image resolution and hardware, limiting high-frequency control tasks","Requires synchronized RGB camera feed; depth or tactile modalities not natively integrated in base architecture"],"requires":["Robot hardware with controllable joint actuators and onboard or networked compute (GPU recommended for real-time inference)","RGB camera(s) providing 256x256 or similar resolution images at 5-10 Hz minimum","Pre-trained RT-1 model weights (released by Google DeepMind) or capability to train on proprietary trajectory dataset","Real-world robot trajectory data with synchronized language annotations for training (minimum 10k+ demonstrations recommended)"],"input_types":["natural language instruction (text string, e.g., 'pick up the red cube and place it in the bin')","RGB image observation from robot camera (256x256 or variable resolution)","optional: previous action history for temporal context"],"output_types":["discretized action tokens (8-bit quantization) representing joint positions, gripper state, and movement magnitude","continuous joint angle targets (post-decoding from tokens)","confidence scores or logits over action space for uncertainty estimation"],"categories":["automation-workflow","planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_1","uri":"capability://automation.workflow.multi.task.robot.policy.learning.from.diverse.demonstrations","name":"multi-task robot policy learning from diverse demonstrations","description":"RT-1 trains a single policy model on a heterogeneous dataset of 130k+ real-world robot trajectories spanning 700+ manipulation tasks (pick-and-place, pushing, rotating, etc.) collected across multiple robot platforms. The architecture uses task-agnostic tokenization and shared transformer weights to learn generalizable manipulation primitives, with language instructions serving as task identifiers and goal specifications. This approach enables the model to interpolate and extrapolate to unseen task combinations without explicit multi-task loss weighting or task-specific heads.","intents":["Train a single robot policy that can handle 700+ different manipulation tasks without separate models per task","Leverage diverse real-world data to improve generalization to novel objects and scene configurations","Reduce deployment complexity by eliminating the need to select or switch between task-specific policies"],"best_for":["robotics labs with access to large-scale real-world trajectory datasets","companies operating multi-task robotic systems (e.g., warehouse automation, manufacturing)","research teams studying emergent generalization in robot learning"],"limitations":["Requires 130k+ diverse, well-annotated demonstrations — prohibitively expensive for most organizations without existing data infrastructure","Performance degrades on tasks significantly different from training distribution; out-of-distribution generalization remains limited","No explicit mechanism for task prioritization or weighting; imbalanced task representation in training data can bias learned policy","Evaluation limited to tabletop manipulation; scaling to mobile manipulation or whole-body control not demonstrated","Requires careful data curation and annotation; noisy or mislabeled trajectories can degrade multi-task performance"],"requires":["Large-scale robot trajectory dataset (130k+ demonstrations minimum) with synchronized RGB images and language task descriptions","Data preprocessing pipeline to standardize action spaces across heterogeneous robot platforms","Compute infrastructure for training (TPU/GPU cluster recommended; training time ~weeks on large datasets)","Language annotation system or template-based task description generation"],"input_types":["robot trajectory dataset: (observation, action, language_instruction) tuples","RGB images from robot camera (256x256 resolution)","action sequences as joint angles or motor commands","natural language task descriptions (free-form or templated)"],"output_types":["trained multi-task policy model (transformer weights)","per-task success metrics and generalization statistics","learned action token embeddings for analysis"],"categories":["automation-workflow","planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_2","uri":"capability://data.processing.analysis.real.world.robot.trajectory.data.collection.and.annotation.pipeline","name":"real-world robot trajectory data collection and annotation pipeline","description":"RT-1 includes infrastructure for collecting synchronized RGB observations, robot joint states, and gripper actions from real robot hardware, paired with natural language task annotations. The pipeline handles temporal alignment across multiple sensor streams, discretizes continuous actions into token bins, and filters or augments trajectories to improve data quality. This enables systematic curation of large-scale, diverse manipulation datasets suitable for training vision-language robot policies.","intents":["Systematically collect and annotate real-world robot manipulation data at scale","Ensure temporal synchronization and quality control across heterogeneous robot platforms","Prepare raw trajectory data for transformer-based policy training with minimal preprocessing overhead"],"best_for":["robotics labs building proprietary manipulation datasets","companies deploying data collection infrastructure for continuous robot learning","research teams studying data efficiency and diversity in robot learning"],"limitations":["Requires custom integration with specific robot hardware and sensor APIs; not a plug-and-play solution","Manual language annotation is labor-intensive; template-based annotation limits task description diversity","No built-in handling for sensor failures, occlusions, or out-of-distribution observations during collection","Trajectory filtering and augmentation heuristics are task-specific; generalization to new domains requires re-tuning","Scalability limited by physical robot availability and human annotation bandwidth"],"requires":["Robot hardware with accessible joint state and gripper control APIs","RGB camera(s) with stable calibration and synchronized timestamping","Data storage infrastructure (minimum 100+ GB for large-scale collection)","Human annotators or templated task description system for language labels","Trajectory preprocessing tools (synchronization, discretization, filtering)"],"input_types":["raw sensor streams: RGB images, joint encoder readings, gripper state","robot control commands (joint targets or motor voltages)","human-provided or templated task descriptions"],"output_types":["synchronized (observation, action, language) trajectory tuples","discretized action tokens (8-bit per dimension)","metadata: task type, object category, success/failure labels","dataset statistics and quality metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_3","uri":"capability://automation.workflow.cross.robot.morphology.action.space.abstraction.and.transfer","name":"cross-robot morphology action space abstraction and transfer","description":"RT-1 abstracts robot-specific action spaces (joint angles, gripper commands) into a unified token-based representation that can be mapped to different robot morphologies. The model learns shared manipulation primitives (e.g., 'reach', 'grasp', 'place') that generalize across robots with different numbers of joints or gripper designs. At inference time, a lightweight morphology-specific decoder translates action tokens back to hardware-specific commands, enabling a single policy to control diverse robot platforms.","intents":["Deploy a single trained policy across multiple robot platforms with different hardware configurations","Learn manipulation skills that transfer to robots not seen during training","Reduce the cost of retraining or fine-tuning policies when deploying to new robot hardware"],"best_for":["companies operating heterogeneous robot fleets (e.g., different arm models from different manufacturers)","robotics labs studying sim-to-real transfer and hardware generalization","developers building robot-agnostic manipulation frameworks"],"limitations":["Generalization limited to robots with similar morphologies (e.g., 6-7 DOF arms); humanoid or quadruped robots not demonstrated","Requires manual specification of action space mapping for each new robot morphology; no automatic morphology discovery","Performance degradation observed when target robot has significantly different kinematic constraints or speed limits","No explicit handling of robot-specific safety constraints (joint limits, collision avoidance) in the abstraction layer","Evaluation limited to tabletop arms; mobile manipulation or whole-body control generalization not demonstrated"],"requires":["Pre-trained RT-1 model weights trained on diverse robot data","Morphology-specific action space mapping (joint ranges, gripper parameters) for target robot","Robot kinematics and control interface (forward kinematics, inverse kinematics optional)","Validation data or simulation environment for testing transfer performance"],"input_types":["action tokens from RT-1 policy (8-bit discretized values)","target robot morphology specification (DOF, joint ranges, gripper type)","optional: current robot state for context-aware decoding"],"output_types":["robot-specific joint angle targets or motor commands","gripper control signals (open/close/position)","optional: confidence scores for action feasibility on target morphology"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_4","uri":"capability://text.generation.language.language.conditioned.task.specification.and.instruction.following","name":"language-conditioned task specification and instruction following","description":"RT-1 conditions its manipulation policy on natural language instructions, using a language encoder (e.g., BERT or similar) to embed task descriptions into a shared representation space with visual observations. The transformer fuses language embeddings with image patches via cross-attention, allowing the policy to interpret diverse phrasings of the same task and adapt behavior based on instruction-specific details (e.g., 'place the red cube in the bin' vs. 'place the blue cube on the table'). This enables interactive task specification without retraining or task-specific policy selection.","intents":["Specify robot manipulation tasks using natural language instead of selecting from a fixed task menu","Enable robots to follow novel task variations and instructions not seen during training","Support interactive task refinement and correction through language feedback"],"best_for":["non-technical users or operators controlling robots via natural language interfaces","research teams studying language grounding in robotics","applications requiring flexible, on-the-fly task specification (e.g., warehouse automation, service robots)"],"limitations":["Generalization to out-of-distribution language phrasings is limited; significant paraphrasing or novel task descriptions may fail","No explicit mechanism for clarifying ambiguous instructions or requesting user feedback when confidence is low","Language encoder is frozen or minimally fine-tuned; adapting to domain-specific terminology requires retraining","Evaluation limited to relatively simple, templated task descriptions; complex multi-step or conditional instructions not demonstrated","No explicit grounding of language to visual scene elements; spatial reasoning (e.g., 'left of', 'behind') may be unreliable"],"requires":["Pre-trained language encoder (BERT, T5, or similar) integrated into RT-1 architecture","Language-annotated robot trajectory dataset (task descriptions paired with demonstrations)","Tokenizer and vocabulary for language encoding (typically 30k-100k tokens)"],"input_types":["natural language task instruction (free-form text string)","RGB image observation from robot camera","optional: previous instruction history for context"],"output_types":["action tokens conditioned on language instruction","confidence scores for instruction understanding","optional: clarification requests or ambiguity flags"],"categories":["text-generation-language","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_5","uri":"capability://planning.reasoning.in.context.learning.and.few.shot.task.adaptation","name":"in-context learning and few-shot task adaptation","description":"RT-1 can adapt to new tasks or objects with minimal additional data by leveraging in-context learning through the transformer's attention mechanism. By conditioning on a few example trajectories or demonstrations in the input context, the policy can adjust its behavior for novel task variations without full retraining. This is enabled by the transformer's ability to attend to demonstration examples and extract task-relevant patterns on-the-fly.","intents":["Adapt the robot policy to new tasks or objects with only a few example demonstrations","Enable rapid task customization without expensive retraining or fine-tuning cycles","Support interactive learning where users can show the robot a few examples of desired behavior"],"best_for":["robotics teams needing rapid task customization and deployment","interactive robot learning scenarios where users provide demonstrations","research on few-shot learning and in-context adaptation in robotics"],"limitations":["In-context learning performance is limited by context window size; typically 1-5 demonstrations are effective, beyond which performance plateaus or degrades","Requires high-quality, representative demonstrations; noisy or atypical examples can mislead the policy","No explicit mechanism for selecting which demonstrations are most informative; random or heuristic selection may be suboptimal","Adaptation is transient; the model weights are not updated, so the adaptation is lost after inference","Evaluation of few-shot adaptation is limited in the paper; generalization bounds and failure modes not thoroughly characterized"],"requires":["Pre-trained RT-1 model with sufficient context window (typically 512-2048 tokens)","Example demonstrations in the form of (observation, action, language) tuples","Ability to format demonstrations as input tokens compatible with the model's tokenizer"],"input_types":["example demonstrations: (RGB image, action sequence, task description) tuples","query task description and current observation","optional: metadata about demonstration quality or relevance"],"output_types":["adapted action tokens for the query task","confidence scores for adaptation quality","optional: attention weights showing which demonstrations influenced the prediction"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_6","uri":"capability://automation.workflow.action.discretization.and.token.based.policy.representation","name":"action discretization and token-based policy representation","description":"RT-1 represents robot actions as discrete tokens (8-bit quantization, 256 bins per dimension) rather than continuous values, enabling the transformer to treat action generation as a categorical prediction problem. This approach leverages the transformer's strength in modeling discrete sequences and allows for efficient beam search or sampling-based action selection. Continuous action values are recovered through decoding, and the discretization granularity can be adjusted to trade off between expressiveness and model capacity.","intents":["Represent robot actions in a format that leverages transformer's categorical prediction strengths","Enable efficient action sampling and beam search for multi-hypothesis action planning","Reduce model capacity requirements by discretizing continuous action spaces"],"best_for":["developers building transformer-based robot policies","research on discrete vs. continuous action representations in deep RL and imitation learning","applications where action quantization is acceptable (most manipulation tasks)"],"limitations":["Discretization introduces quantization error; fine-grained control tasks may suffer from reduced precision","8-bit quantization (256 bins) may be insufficient for high-DOF robots or tasks requiring sub-millimeter accuracy","Decoding from tokens to continuous actions requires careful calibration; mismatch between training and inference can degrade performance","No adaptive quantization; bin sizes are fixed and may not be optimal for all action dimensions (e.g., gripper open/close vs. joint angles)","Beam search or sampling-based action selection adds inference latency compared to direct continuous prediction"],"requires":["Action space specification: joint ranges, gripper parameters, and desired discretization granularity","Quantization and dequantization functions for converting between continuous and discrete action spaces","Training data with action sequences aligned to discretized bins"],"input_types":["continuous action sequences from robot trajectories","action space bounds and dimensionality"],"output_types":["discretized action tokens (8-bit integers, 0-255 per dimension)","logits or probabilities over action token bins","decoded continuous action values (post-inference)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_7","uri":"capability://image.visual.visual.observation.encoding.with.patch.based.tokenization","name":"visual observation encoding with patch-based tokenization","description":"RT-1 encodes RGB images as sequences of visual tokens by dividing images into patches (e.g., 16x16 pixel patches) and embedding each patch independently, similar to Vision Transformer (ViT) architecture. These visual tokens are then fused with language tokens via cross-attention in the transformer, enabling the policy to attend to task-relevant image regions. The patch-based approach reduces computational complexity compared to pixel-level processing and enables efficient spatial reasoning over the visual scene.","intents":["Efficiently encode RGB observations for transformer-based robot policies","Enable spatial attention over image regions relevant to manipulation tasks","Reduce computational cost of visual processing compared to pixel-level or CNN-based approaches"],"best_for":["developers building vision-language robot policies","research on efficient visual encoding for robotics","applications with computational constraints (edge deployment)"],"limitations":["Patch size is a hyperparameter; too large patches lose fine-grained details, too small patches increase sequence length and computation","No explicit handling of occlusions or out-of-focus regions; patch embeddings are computed uniformly across the image","Positional encoding of patches assumes regular grid structure; irregular or non-Euclidean visual inputs not supported","Visual encoding is frozen or minimally fine-tuned; adapting to domain-specific visual features (e.g., reflective surfaces, low-light) requires retraining","Evaluation limited to 256x256 RGB images; scalability to higher resolutions or multi-camera setups not thoroughly characterized"],"requires":["RGB image input at fixed resolution (e.g., 256x256)","Patch embedding layer (linear projection of flattened patches)","Positional encoding for patch positions (learnable or fixed sinusoidal)"],"input_types":["RGB image (256x256 or variable resolution)","optional: image preprocessing (normalization, augmentation)"],"output_types":["visual token embeddings (sequence of patch embeddings)","optional: attention weights over patches for interpretability"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_8","uri":"capability://planning.reasoning.transformer.based.policy.architecture.with.cross.attention.fusion","name":"transformer-based policy architecture with cross-attention fusion","description":"RT-1 uses a transformer encoder-decoder architecture where language and visual tokens are processed through separate embedding streams and fused via cross-attention mechanisms. The decoder generates action tokens autoregressively, attending to both language and visual context at each step. This architecture enables joint reasoning over language and vision, allowing the policy to ground task instructions in visual observations and generate contextually appropriate actions.","intents":["Build a unified policy model that jointly reasons over language instructions and visual observations","Enable flexible task specification and visual grounding without separate language and vision modules","Leverage transformer's attention mechanisms for interpretable, spatially-aware action generation"],"best_for":["robotics researchers building vision-language policies","developers implementing transformer-based robot control systems","teams studying attention mechanisms and interpretability in robot learning"],"limitations":["Transformer architecture introduces quadratic complexity in sequence length; long action sequences or high-resolution images can be computationally expensive","Cross-attention fusion is learned end-to-end; no explicit mechanism for controlling the balance between language and visual conditioning","Autoregressive action generation can accumulate errors over long horizons; no explicit error correction or replanning mechanism","Evaluation limited to relatively short action sequences (typically <10 steps); long-horizon manipulation tasks not demonstrated","Interpretability of cross-attention is limited; understanding which visual regions or language tokens influence specific actions requires post-hoc analysis"],"requires":["Transformer encoder-decoder architecture (e.g., based on T5 or similar)","Language encoder (BERT or similar) for embedding task instructions","Visual encoder for patch-based image tokenization","Cross-attention layers for fusing language and visual representations","Action decoder for generating action tokens autoregressively"],"input_types":["language instruction tokens (from language encoder)","visual tokens (from patch-based image encoder)","optional: previous action tokens for autoregressive generation"],"output_types":["action token logits (probabilities over discretized action bins)","optional: attention weights for interpretability","optional: intermediate representations for analysis"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1__cap_9","uri":"capability://automation.workflow.sim.to.real.transfer.and.domain.randomization.for.robot.learning","name":"sim-to-real transfer and domain randomization for robot learning","description":"RT-1 leverages domain randomization and simulation-based pre-training to improve sample efficiency and generalization to real-world robot hardware. The approach involves training policies in simulation with randomized visual appearance, lighting, and object properties, then fine-tuning on real-world data. This reduces the amount of real-world data required and improves robustness to visual distribution shifts and hardware variations.","intents":["Reduce the amount of real-world robot data required for training by leveraging simulation pre-training","Improve generalization to visual distribution shifts and hardware variations through domain randomization","Accelerate robot learning by combining simulation and real-world data efficiently"],"best_for":["robotics teams with access to simulation environments and real robot hardware","companies looking to reduce data collection costs for robot learning","research on sim-to-real transfer and domain adaptation in robotics"],"limitations":["Requires high-fidelity simulation environment; physics simulation errors can propagate to real-world performance","Domain randomization hyperparameters are task-specific; generalization to new domains requires re-tuning","Sim-to-real gap remains significant for tasks with complex contact dynamics or precise manipulation requirements","Evaluation of sim-to-real transfer is limited in the paper; quantitative analysis of data efficiency gains not provided","Requires dual infrastructure (simulation and real robots); not practical for teams with only real hardware"],"requires":["High-fidelity simulation environment (e.g., PyBullet, MuJoCo, or similar) with physics simulation","Domain randomization configuration (visual appearance, lighting, object properties, physics parameters)","Real robot hardware for fine-tuning and evaluation","Transfer learning pipeline for adapting simulation-trained models to real-world data"],"input_types":["simulated robot trajectories with randomized visual appearance and physics","real-world robot trajectories for fine-tuning","domain randomization parameters (ranges for visual and physics properties)"],"output_types":["simulation-pre-trained policy model","fine-tuned real-world policy model","sim-to-real transfer metrics and analysis"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":18,"verified":false,"data_access_risk":"low","permissions":["Robot hardware with controllable joint actuators and onboard or networked compute (GPU recommended for real-time inference)","RGB camera(s) providing 256x256 or similar resolution images at 5-10 Hz minimum","Pre-trained RT-1 model weights (released by Google DeepMind) or capability to train on proprietary trajectory dataset","Real-world robot trajectory data with synchronized language annotations for training (minimum 10k+ demonstrations recommended)","Large-scale robot trajectory dataset (130k+ demonstrations minimum) with synchronized RGB images and language task descriptions","Data preprocessing pipeline to standardize action spaces across heterogeneous robot platforms","Compute infrastructure for training (TPU/GPU cluster recommended; training time ~weeks on large datasets)","Language annotation system or template-based task description generation","Robot hardware with accessible joint state and gripper control APIs","RGB camera(s) with stable calibration and synchronized timestamping"],"failure_modes":["Requires large-scale diverse robot trajectory data (RT-1 trained on 130k+ real-world demonstrations) — not practical for single-robot setups","Discretized action space limits fine-grained control precision; continuous action variants require separate training","Generalization to significantly different robot morphologies (e.g., humanoid vs. industrial arm) not demonstrated; primarily validated on similar arm configurations","Inference latency ~200-500ms per action step depending on image resolution and hardware, limiting high-frequency control tasks","Requires synchronized RGB camera feed; depth or tactile modalities not natively integrated in base architecture","Requires 130k+ diverse, well-annotated demonstrations — prohibitively expensive for most organizations without existing data infrastructure","Performance degrades on tasks significantly different from training distribution; out-of-distribution generalization remains limited","No explicit mechanism for task prioritization or weighting; imbalanced task representation in training data can bias learned policy","Evaluation limited to tabletop manipulation; scaling to mobile manipulation or whole-body control not demonstrated","Requires careful data curation and annotation; noisy or mislabeled trajectories can degrade multi-task performance","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.35,"quality":0.2,"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":"inactive","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:27.894Z","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=rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","compare_url":"https://unfragile.ai/compare?artifact=rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1"}},"signature":"KtWAC1j4qdl17fwo4YoniOXC29mpjSGjgX7L7v/UVnxT89DqhdiuembwyCNRJPdYWlLtMxQswsx1vv+uhUW8Bw==","signedAt":"2026-06-19T19:32:27.351Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","artifact":"https://unfragile.ai/rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","verify":"https://unfragile.ai/api/v1/verify?slug=rt-1-robotics-transformer-for-real-world-control-at-scale-rt-1","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"}}