{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"octo","slug":"octo","name":"Octo","type":"repo","url":"https://github.com/octo-models/octo","page_url":"https://unfragile.ai/octo","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"octo__cap_0","uri":"capability://planning.reasoning.pretrained.generalist.robot.policy.inference.with.multimodal.task.specification","name":"pretrained generalist robot policy inference with multimodal task specification","description":"Loads a pretrained OctoModel trained on 800K diverse robot trajectories from Open X-Embodiment dataset and performs action prediction by processing multimodal inputs (camera observations, proprioception, language instructions or goal images) through a causal transformer backbone followed by action head decoding. The model uses tokenized representations of observations and task specifications, processes them through the OctoTransformer's attention layers, and outputs continuous action distributions via diffusion or L1 action heads.","intents":["Load a pretrained Octo model and immediately run inference on my robot without retraining","Predict robot actions from multiple camera views and natural language task descriptions","Get action predictions conditioned on visual goal images rather than language instructions","Sample multiple action trajectories from the model's learned action distribution for ensemble-based control"],"best_for":["Robotics researchers prototyping new tasks on existing robot platforms","Teams deploying manipulation policies to physical robots with minimal data collection","Developers building multi-embodiment robot applications leveraging transfer learning"],"limitations":["Pretrained model performance degrades on robot morphologies significantly different from training distribution (e.g., humanoid vs quadruped)","Inference latency depends on transformer sequence length and action head type; diffusion heads require multiple sampling steps (~100-500ms per action)","Model expects standardized observation tokenization; custom sensor modalities require implementing new tokenizer classes","No built-in uncertainty quantification beyond action distribution sampling; confidence scores require external calibration"],"requires":["Python 3.9+","PyTorch 2.0+ with CUDA support recommended for real-time inference","Pretrained model checkpoint (provided in repository or custom fine-tuned checkpoint)","Robot environment compatible with gym interface or custom wrapper implementation","Camera observations in standard formats (uint8 images) and proprioceptive state vectors"],"input_types":["RGB/grayscale images (multiple camera views)","Proprioceptive state (joint positions, velocities, gripper state)","Natural language task descriptions (text strings)","Goal images (RGB images specifying desired end state)"],"output_types":["Continuous action vectors (joint positions, velocities, or torques)","Action probability distributions (for diffusion-based heads)","Sampled action trajectories (multiple rollouts for ensemble methods)"],"categories":["planning-reasoning","robotics-policy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_1","uri":"capability://model.training.efficient.fine.tuning.for.new.robot.embodiments.and.observation.action.spaces","name":"efficient fine-tuning for new robot embodiments and observation-action spaces","description":"Adapts pretrained Octo models to new robot morphologies and sensor configurations through parameter-efficient fine-tuning that reuses the transformer backbone while replacing or retraining tokenizers and action heads. The system supports selective layer freezing, custom observation/action tokenizer training, and task-specific data augmentation, enabling adaptation with 10-100x less data than training from scratch.","intents":["Fine-tune a pretrained model for my specific robot with only 100-500 demonstration trajectories","Adapt the model to a new camera setup or proprioceptive sensor configuration","Retrain action heads for different action spaces (e.g., from joint positions to end-effector velocities)","Customize task tokenizers to handle domain-specific language or visual goal specifications"],"best_for":["Robotics teams with limited demonstration data for new robot platforms","Researchers exploring embodiment transfer and morphology generalization","Companies deploying Octo to proprietary robots with custom sensor suites"],"limitations":["Fine-tuning requires careful hyperparameter tuning; learning rate and batch size significantly impact convergence on small datasets","Catastrophic forgetting can occur if fine-tuning data distribution diverges too far from pretraining; requires regularization or careful layer freezing","Custom tokenizers must be trained from scratch if observation/action spaces are fundamentally different; no automatic tokenizer adaptation","Fine-tuning on <50 trajectories often leads to overfitting; data augmentation strategies are dataset-dependent and not automated"],"requires":["Python 3.9+","PyTorch 2.0+ with GPU memory ≥16GB for efficient fine-tuning","Pretrained Octo checkpoint","10-500 demonstration trajectories from target robot in standardized format (HDF5 or similar)","Robot environment specification (observation/action space definitions, tokenizer configurations)"],"input_types":["Demonstration trajectories (sequences of observations, actions, rewards)","Robot specification (morphology, action space, sensor modalities)","Fine-tuning hyperparameters (learning rate, batch size, layer freeze masks)","Optional: task augmentation parameters (language paraphrasing, image transformations)"],"output_types":["Fine-tuned model checkpoint with adapted tokenizers and action heads","Training logs and metrics (loss curves, validation performance)","Inference-ready model compatible with deployment wrappers"],"categories":["model-training","robotics-policy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_10","uri":"capability://automation.workflow.real.robot.deployment.with.closed.loop.control.and.monitoring","name":"real robot deployment with closed-loop control and monitoring","description":"Enables deployment of Octo policies to physical robots through standardized control loops that execute actions, collect observations, and monitor performance in real-time. Supports multiple control modes (open-loop trajectory execution, closed-loop feedback control, receding horizon control) and provides hooks for safety monitoring, action filtering, and emergency stops.","intents":["Deploy Octo policies to physical robots in real-time control loops","Execute actions with closed-loop feedback to correct for model errors","Monitor policy performance and detect failures during deployment","Implement safety mechanisms (action filtering, emergency stops) for safe robot operation"],"best_for":["Robotics teams deploying policies to physical manipulation robots","Researchers studying real-world policy performance and failure modes","Companies building production robot systems with safety requirements"],"limitations":["Real-world deployment requires careful tuning of control parameters (action scaling, feedback gains); suboptimal tuning can cause instability or poor performance","Network latency and sensor delays can cause control instability; policies trained with zero latency may fail with real-world delays (50-200ms)","Safety mechanisms (action filtering, emergency stops) require task-specific implementation; no automatic safety guarantees","Real-world observations often differ from training distribution (lighting, object appearance, camera calibration); policies may fail without domain adaptation"],"requires":["Python 3.9+","PyTorch 2.0+ with real-time performance requirements (inference latency <100ms)","Robot hardware with compatible control interface (ROS, custom APIs)","Sensor drivers (cameras, proprioceptive sensors) with low-latency access","Pretrained or fine-tuned Octo model checkpoint optimized for inference speed","Safety mechanisms (emergency stop button, action limits, collision detection)"],"input_types":["Real-time sensor observations (camera images, proprioceptive state)","Task specification (language instructions or goal images)","Control parameters (action scaling, feedback gains, control frequency)","Safety constraints (action limits, collision detection thresholds)"],"output_types":["Robot actions (joint positions, velocities, or torques)","Control loop metrics (inference latency, action execution time)","Deployment logs (observations, actions, rewards, failures)","Safety alerts (action limit violations, collision detections)"],"categories":["automation-workflow","robotics-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_11","uri":"capability://automation.workflow.training.callbacks.and.monitoring.for.model.development","name":"training callbacks and monitoring for model development","description":"Provides extensible callback system for monitoring training progress, logging metrics, and triggering actions during training (e.g., checkpointing, evaluation, learning rate scheduling). Callbacks integrate with standard logging frameworks (Weights & Biases, TensorBoard) and support custom metrics computation (action prediction accuracy, trajectory success rates in simulation).","intents":["Monitor training progress through real-time metrics logging and visualization","Automatically checkpoint models at regular intervals or when validation metrics improve","Evaluate policies on validation tasks during training without interrupting training","Implement custom learning rate schedules or early stopping based on validation metrics"],"best_for":["Robotics researchers training models and monitoring convergence","Teams implementing hyperparameter tuning and model selection","Developers building automated training pipelines with minimal manual intervention"],"limitations":["Callback execution adds overhead to training loop; frequent callbacks (e.g., per-batch) can reduce training throughput by 5-20%","Custom metrics computation requires task-specific implementation; no automatic metrics for arbitrary tasks","Logging to external services (W&B, TensorBoard) requires network connectivity; offline training requires local logging","Callback composition can be complex; multiple callbacks with interdependencies can cause unexpected behavior"],"requires":["Python 3.9+","PyTorch 2.0+ with training loop integration","Optional: Weights & Biases or TensorBoard for metric visualization","Training configuration (checkpoint frequency, validation schedule)","Custom callback implementations (if using non-standard metrics)"],"input_types":["Training metrics (loss, accuracy, validation performance)","Model state (weights, optimizer state)","Training configuration (learning rate, batch size, number of epochs)","Validation data (for computing validation metrics)"],"output_types":["Logged metrics (training loss, validation accuracy, learning rate)","Model checkpoints (saved weights at regular intervals)","Visualization dashboards (W&B, TensorBoard)","Training reports (final metrics, convergence analysis)"],"categories":["automation-workflow","robotics-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_12","uri":"capability://data.processing.analysis.model.evaluation.metrics.and.visualization.for.policy.analysis","name":"model evaluation metrics and visualization for policy analysis","description":"Computes quantitative metrics for policy evaluation (action prediction accuracy, trajectory success rates, action smoothness, task completion time) and provides visualization tools (trajectory playback, attention weight visualization, action distribution plots). Metrics are computed on validation datasets or in simulation, enabling quantitative comparison of policies and identification of failure modes.","intents":["Measure policy performance using standardized metrics (success rate, trajectory length, action smoothness)","Visualize policy behavior through trajectory playback and attention weight visualization","Compare policies quantitatively to identify improvements and failure modes","Analyze action distributions and uncertainty estimates for policy debugging"],"best_for":["Robotics researchers analyzing policy performance and failure modes","Teams comparing different model architectures and training strategies","Developers building policy evaluation and analysis tools"],"limitations":["Metrics are task-specific; no universal metrics that apply to all robot tasks","Visualization tools require significant computational resources; rendering trajectories for large datasets can be slow","Attention weight visualization assumes interpretable attention patterns; attention may not correspond to task-relevant features","Action distribution analysis requires access to model internals; some metrics may not be available for deployed models"],"requires":["Python 3.9+","PyTorch 2.0+","Validation dataset or simulation environment","Pretrained or fine-tuned Octo model checkpoint","Optional: visualization libraries (matplotlib, plotly) for custom visualizations"],"input_types":["Validation trajectories (observations, actions, rewards)","Model predictions (actions, attention weights, action distributions)","Task specifications (success criteria, metrics definitions)","Visualization parameters (trajectory selection, attention layer selection)"],"output_types":["Quantitative metrics (success rate, trajectory length, action smoothness)","Trajectory visualizations (rendered videos, 2D plots)","Attention weight visualizations (heatmaps, attention flow diagrams)","Action distribution plots (histograms, scatter plots)"],"categories":["data-processing-analysis","robotics-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_2","uri":"capability://data.processing.analysis.multimodal.observation.tokenization.with.flexible.sensor.composition","name":"multimodal observation tokenization with flexible sensor composition","description":"Converts heterogeneous robot sensor inputs (RGB/grayscale images from multiple cameras, proprioceptive state vectors, depth maps) into fixed-size token sequences using modular tokenizer components (image tokenizers via learned codebooks or pretrained vision models, proprioception tokenizers via linear projections or MLPs). Tokenizers are composed in a pipeline that handles variable numbers of cameras and sensor modalities, enabling the transformer to process observations in a unified sequence format.","intents":["Tokenize multi-camera observations into a fixed-size representation for transformer processing","Handle robots with different numbers of cameras without retraining the transformer","Combine proprioceptive state (joint angles, velocities) with visual observations in a unified token sequence","Implement custom tokenizers for specialized sensors (depth cameras, tactile sensors, IMU data)"],"best_for":["Robotics engineers building perception pipelines for diverse sensor configurations","Researchers studying how different observation modalities affect policy learning","Teams deploying policies across robots with heterogeneous sensor suites"],"limitations":["Image tokenizers require careful tuning of codebook size and token dimensions; too few tokens lose visual information, too many increase latency","Proprioception tokenizers assume normalized input; non-normalized state vectors can cause training instability","No automatic sensor fusion; combining modalities requires manual specification of token concatenation order and weighting","Tokenizer training is dataset-specific; pretrained image tokenizers may not generalize to out-of-distribution visual conditions (e.g., different lighting, camera angles)"],"requires":["Python 3.9+","PyTorch 2.0+","Observation specification defining camera count, resolution, proprioceptive state dimensions","Optional: pretrained vision model (e.g., ResNet, ViT) for image tokenization","Training data with diverse observations for tokenizer training (if not using pretrained tokenizers)"],"input_types":["RGB/grayscale images (variable resolution, multiple cameras)","Proprioceptive state vectors (joint positions, velocities, gripper state)","Optional: depth maps, segmentation masks, other auxiliary observations"],"output_types":["Fixed-size token sequences (e.g., 512 tokens per observation)","Token embeddings (learned representations in embedding space)","Tokenizer configuration (codebook definitions, projection matrices)"],"categories":["data-processing-analysis","robotics-perception"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_3","uri":"capability://text.generation.language.task.specification.encoding.with.language.and.visual.goal.conditioning","name":"task specification encoding with language and visual goal conditioning","description":"Encodes task specifications (natural language instructions or goal images) into token sequences using task-specific tokenizers (language tokenizers via pretrained text models like BERT, goal image tokenizers via vision models). These task tokens are concatenated with observation tokens in the transformer input sequence, enabling the model to condition action prediction on either linguistic task descriptions or visual goal states without architectural changes.","intents":["Specify robot tasks using natural language instructions (e.g., 'pick up the red cube')","Condition policies on visual goal images showing desired end states","Switch between language and visual goal conditioning at inference time","Fine-tune task tokenizers to handle domain-specific language or visual concepts"],"best_for":["Robotics teams building language-conditioned manipulation policies","Researchers studying vision-language grounding in robot control","Applications requiring flexible task specification (e.g., human-in-the-loop systems)"],"limitations":["Language tokenizers require pretraining on large text corpora; domain-specific robot language may not be well-represented in pretrained models","Visual goal conditioning assumes goal images are from the same camera viewpoint and lighting conditions as training data; distribution shift degrades performance","No automatic task augmentation; paraphrasing language instructions or transforming goal images requires manual specification","Task tokenizers are frozen during inference; cannot adapt to novel task phrasings or visual styles without retraining"],"requires":["Python 3.9+","PyTorch 2.0+","Pretrained language model (e.g., BERT, GPT-2) for language tokenization","Pretrained vision model (e.g., ResNet, ViT) for goal image tokenization","Task specification format (text strings or goal images) matching training data distribution"],"input_types":["Natural language task descriptions (text strings, variable length)","Goal images (RGB images showing desired end state)","Optional: task metadata (object names, spatial relationships)"],"output_types":["Task token sequences (fixed-size or variable-length depending on tokenizer)","Task embeddings (learned representations in embedding space)","Task-conditioned action distributions (actions sampled conditioned on task tokens)"],"categories":["text-generation-language","robotics-policy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_4","uri":"capability://planning.reasoning.causal.transformer.backbone.for.sequential.action.prediction","name":"causal transformer backbone for sequential action prediction","description":"Processes tokenized observation and task sequences through a causal transformer architecture (OctoTransformer) that applies masked self-attention to prevent attending to future tokens, enabling autoregressive action prediction. The transformer uses standard components (multi-head attention, feedforward layers, layer normalization) with causal masking to ensure actions depend only on past and current observations, not future information.","intents":["Process variable-length observation sequences through a unified transformer backbone","Predict actions autoregressively, one timestep at a time, in a control loop","Leverage transformer's ability to model long-range dependencies in robot trajectories","Share learned representations across different tasks and embodiments via the transformer backbone"],"best_for":["Robotics researchers building sequence models for robot control","Teams deploying policies that require long-horizon reasoning (multi-step tasks)","Applications where transfer learning across embodiments is critical"],"limitations":["Causal masking prevents the model from using future information, which can be suboptimal for offline planning or trajectory optimization","Transformer inference latency scales with sequence length; long observation histories (>100 timesteps) can cause 100-500ms delays","Attention mechanism has O(n²) complexity; very long sequences (>1000 tokens) become computationally prohibitive","Model learns from data distribution; out-of-distribution observations can cause erratic action predictions without uncertainty quantification"],"requires":["Python 3.9+","PyTorch 2.0+ with CUDA support for efficient attention computation","Tokenized observation and task sequences (from observation and task tokenizers)","Transformer configuration (hidden dimension, number of layers, number of attention heads)","Training data with diverse trajectories to learn generalizable representations"],"input_types":["Tokenized observation sequences (variable length, fixed token dimension)","Tokenized task specifications (language or visual goal tokens)","Optional: action history for context (previous actions in the sequence)"],"output_types":["Transformer hidden states (learned representations at each timestep)","Action head inputs (features for downstream action prediction)","Attention weights (for interpretability and debugging)"],"categories":["planning-reasoning","robotics-policy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_5","uri":"capability://planning.reasoning.action.head.decoding.with.diffusion.and.l1.regression","name":"action head decoding with diffusion and l1 regression","description":"Decodes transformer hidden states into robot actions using pluggable action heads that support diffusion-based action prediction (iterative denoising of action distributions) or L1 regression (direct action prediction). Diffusion heads enable multi-modal action distributions and uncertainty quantification, while L1 heads provide deterministic, low-latency action prediction. Both heads are trained jointly with the transformer backbone.","intents":["Predict continuous robot actions (joint positions, velocities, or end-effector poses) from transformer representations","Sample multiple action hypotheses from learned action distributions for ensemble-based control","Quantify action uncertainty through diffusion-based sampling or prediction variance","Switch between stochastic (diffusion) and deterministic (L1) action prediction at inference time"],"best_for":["Robotics teams building policies that require action uncertainty quantification","Applications using ensemble methods or multi-hypothesis planning","Researchers studying multimodal action distributions in imitation learning"],"limitations":["Diffusion heads require multiple denoising steps (50-500) per action, adding 100-500ms latency compared to L1 heads (~10-50ms)","L1 regression assumes unimodal action distributions; fails on tasks with multiple valid action modes (e.g., grasping from different angles)","Both heads require careful tuning of action normalization; non-normalized actions can cause training instability or poor generalization","Action head outputs are deterministic given transformer features; no online adaptation or uncertainty-driven exploration"],"requires":["Python 3.9+","PyTorch 2.0+","Transformer hidden states from OctoTransformer","Action space specification (dimensionality, bounds, normalization parameters)","Training data with diverse action distributions (for diffusion head training)"],"input_types":["Transformer hidden states (fixed dimension, e.g., 256 or 512)","Action space specification (continuous action bounds, normalization)","Optional: action history (previous actions for context)"],"output_types":["Continuous action vectors (joint positions, velocities, or torques)","Action probability distributions (for diffusion heads)","Sampled action trajectories (multiple rollouts for ensemble methods)","Action uncertainty estimates (variance or entropy)"],"categories":["planning-reasoning","robotics-policy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_6","uri":"capability://data.processing.analysis.open.x.embodiment.dataset.loading.and.preprocessing","name":"open x-embodiment dataset loading and preprocessing","description":"Loads and preprocesses the Open X-Embodiment dataset (800K robot trajectories across 22+ platforms) through a standardized data pipeline that handles heterogeneous data formats (HDF5, TFRecord, RLDS), performs observation normalization, action space conversion, and trajectory filtering. The data system supports lazy loading and on-the-fly augmentation to handle the dataset's scale and diversity.","intents":["Load diverse robot trajectory data from multiple sources and formats into a unified training pipeline","Normalize observations and actions across different robot platforms for consistent training","Filter trajectories by task, robot type, or quality metrics to create task-specific training sets","Apply data augmentation (image transformations, action noise) to improve generalization"],"best_for":["Robotics researchers training generalist policies on large-scale diverse datasets","Teams building custom datasets compatible with Octo's data format","Developers implementing dataset-specific preprocessing pipelines"],"limitations":["Dataset loading requires significant disk I/O; training on full 800K dataset requires 500GB+ storage and careful batching to avoid bottlenecks","Heterogeneous data formats require format-specific loaders; adding new data sources requires implementing custom dataset classes","Observation normalization is dataset-dependent; statistics computed on training set may not generalize to new robots or environments","Trajectory filtering and augmentation parameters are manually specified; no automatic heuristics for optimal filtering or augmentation strategies"],"requires":["Python 3.9+","PyTorch 2.0+ with DataLoader support","Open X-Embodiment dataset (800K trajectories, ~500GB storage) or custom dataset in compatible format","Dataset specification (observation/action space definitions, normalization statistics)","Optional: TensorFlow for RLDS format support"],"input_types":["Robot trajectory data (HDF5, TFRecord, RLDS formats)","Observation and action space specifications","Trajectory metadata (task labels, robot type, environment)","Optional: augmentation parameters (image transformations, action noise)"],"output_types":["Batched training data (observations, actions, task specifications)","Normalized observations and actions (standardized to zero mean, unit variance)","Trajectory metadata (for filtering and analysis)","Data statistics (normalization parameters, trajectory length distributions)"],"categories":["data-processing-analysis","robotics-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_7","uri":"capability://data.processing.analysis.data.transformation.and.task.augmentation.pipeline","name":"data transformation and task augmentation pipeline","description":"Applies configurable transformations to training data including observation normalization, action space conversion, image augmentation (resizing, cropping, color jittering), and task augmentation (language paraphrasing, goal image transformations). Transformations are composed in a pipeline that can be applied during data loading or training, enabling efficient on-the-fly augmentation without storing augmented data.","intents":["Normalize observations and actions to zero mean and unit variance for stable training","Apply image augmentations (resizing, cropping, color jittering) to improve visual robustness","Paraphrase language task descriptions to increase linguistic diversity in training data","Transform goal images (rotation, scaling, color shifts) to improve visual goal generalization"],"best_for":["Robotics researchers improving policy generalization through data augmentation","Teams adapting pretrained models to new visual conditions or language variations","Developers implementing custom data transformations for specialized sensors or tasks"],"limitations":["Augmentation parameters are manually specified; no automatic heuristics for optimal augmentation strength or composition","Language paraphrasing requires pretrained models (e.g., T5, GPT-2); quality depends on model's understanding of robot tasks","Image augmentations can corrupt task-relevant information if applied too aggressively (e.g., aggressive cropping removing objects)","Transformation pipeline is applied uniformly to all data; no per-sample or per-task adaptive augmentation"],"requires":["Python 3.9+","PyTorch 2.0+ with torchvision for image augmentations","Optional: pretrained language models (T5, GPT-2) for language paraphrasing","Observation and action space specifications (for normalization)","Augmentation configuration (image transformation parameters, paraphrasing models)"],"input_types":["Raw observations (images, proprioceptive state)","Raw actions (joint positions, velocities, or torques)","Task specifications (language instructions or goal images)","Augmentation parameters (transformation strengths, model choices)"],"output_types":["Normalized observations and actions","Augmented images (resized, cropped, color-jittered)","Paraphrased task descriptions","Transformed goal images"],"categories":["data-processing-analysis","robotics-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_8","uri":"capability://tool.use.integration.gym.environment.wrapper.interface.for.robot.deployment","name":"gym environment wrapper interface for robot deployment","description":"Provides standardized gym-compatible wrappers (NormalizeProprio, HistoryWrapper, RHCWrapper) that interface Octo policies with robot environments and simulators. Wrappers handle observation normalization, history buffering for temporal context, and receding horizon control (RHC) for closed-loop execution. This abstraction enables the same policy code to work across different robot platforms and simulators.","intents":["Deploy Octo policies to robot environments using standard gym interface","Normalize proprioceptive observations on-the-fly during deployment","Maintain observation history for temporal context in control loops","Implement receding horizon control (RHC) for improved closed-loop performance"],"best_for":["Robotics teams deploying policies to physical robots or simulators","Researchers comparing policies across different robot platforms","Developers building robot control systems with standardized interfaces"],"limitations":["Wrappers assume gym-compatible environment interface; non-standard robot APIs require custom wrapper implementation","Observation normalization requires precomputed statistics (mean, std); statistics computed on training data may not generalize to deployment environments","History buffering adds latency proportional to history length; long histories (>10 timesteps) can cause 50-200ms delays","RHC wrapper assumes deterministic environment dynamics; stochastic environments may require adaptive control strategies"],"requires":["Python 3.9+","PyTorch 2.0+","Gym-compatible environment (or custom wrapper for non-standard APIs)","Observation normalization statistics (mean, std computed on training data)","Pretrained or fine-tuned Octo model checkpoint"],"input_types":["Gym environment (with reset() and step() methods)","Normalization statistics (observation mean and std)","History buffer size (number of past observations to maintain)","RHC parameters (planning horizon, action repeat count)"],"output_types":["Normalized observations (zero mean, unit variance)","Observation history (buffered past observations)","Robot actions (executed via environment.step())","Deployment metrics (episode returns, success rates)"],"categories":["tool-use-integration","robotics-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__cap_9","uri":"capability://tool.use.integration.simulation.environment.integration.for.policy.evaluation.and.training","name":"simulation environment integration for policy evaluation and training","description":"Integrates with simulation environments (MuJoCo, PyBullet, IsaacGym) through gym-compatible wrappers, enabling policy evaluation in simulation before deployment to physical robots. Supports rendering, trajectory logging, and metrics collection (success rates, trajectory lengths, action smoothness) for quantitative policy evaluation.","intents":["Evaluate policies in simulation before deploying to physical robots","Collect simulation trajectories for additional fine-tuning data","Visualize policy behavior through rendering and trajectory playback","Measure policy performance using standardized metrics (success rate, trajectory length)"],"best_for":["Robotics teams validating policies in simulation before real-world deployment","Researchers studying sim-to-real transfer and domain randomization","Developers building simulation-based evaluation pipelines"],"limitations":["Simulation-to-reality gap can cause significant performance degradation; policies trained in simulation may fail on physical robots","Rendering and trajectory logging add computational overhead; can reduce evaluation throughput by 2-10x","Metrics collection requires task-specific reward functions or success criteria; no automatic metric computation","Different simulators have different physics engines and rendering pipelines; policies may not transfer across simulators"],"requires":["Python 3.9+","PyTorch 2.0+","Simulation environment (MuJoCo, PyBullet, IsaacGym) with gym interface","Robot model and environment configuration (URDF, XML)","Pretrained or fine-tuned Octo model checkpoint"],"input_types":["Simulation environment (gym-compatible)","Robot model (URDF, XML, or simulator-native format)","Task specification (goal state, reward function, success criteria)","Evaluation parameters (number of episodes, rendering options)"],"output_types":["Episode trajectories (observations, actions, rewards)","Evaluation metrics (success rate, trajectory length, action smoothness)","Rendered videos (optional, for visualization)","Trajectory logs (for analysis and debugging)"],"categories":["tool-use-integration","robotics-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"octo__headline","uri":"capability://model.training.generalist.robotic.policy.training.framework","name":"generalist robotic policy training framework","description":"Octo is an open-source framework designed for training and deploying generalist robotic policies using transformer-based models, enabling robots to learn from diverse datasets and perform various tasks with minimal additional training.","intents":["best framework for robotic policy training","generalist robotic model for diverse tasks","open-source robotic manipulation training","transformer-based model for robot control","robotic policy fine-tuning framework"],"best_for":["researchers in robotics","developers creating robotic applications"],"limitations":[],"requires":["access to the Open X-Embodiment dataset"],"input_types":["robotic sensor data","language instructions"],"output_types":["robot actions","task specifications"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","PyTorch 2.0+ with CUDA support recommended for real-time inference","Pretrained model checkpoint (provided in repository or custom fine-tuned checkpoint)","Robot environment compatible with gym interface or custom wrapper implementation","Camera observations in standard formats (uint8 images) and proprioceptive state vectors","PyTorch 2.0+ with GPU memory ≥16GB for efficient fine-tuning","Pretrained Octo checkpoint","10-500 demonstration trajectories from target robot in standardized format (HDF5 or similar)","Robot environment specification (observation/action space definitions, tokenizer configurations)","PyTorch 2.0+ with real-time performance requirements (inference latency <100ms)"],"failure_modes":["Pretrained model performance degrades on robot morphologies significantly different from training distribution (e.g., humanoid vs quadruped)","Inference latency depends on transformer sequence length and action head type; diffusion heads require multiple sampling steps (~100-500ms per action)","Model expects standardized observation tokenization; custom sensor modalities require implementing new tokenizer classes","No built-in uncertainty quantification beyond action distribution sampling; confidence scores require external calibration","Fine-tuning requires careful hyperparameter tuning; learning rate and batch size significantly impact convergence on small datasets","Catastrophic forgetting can occur if fine-tuning data distribution diverges too far from pretraining; requires regularization or careful layer freezing","Custom tokenizers must be trained from scratch if observation/action spaces are fundamentally different; no automatic tokenizer adaptation","Fine-tuning on <50 trajectories often leads to overfitting; data augmentation strategies are dataset-dependent and not automated","Real-world deployment requires careful tuning of control parameters (action scaling, feedback gains); suboptimal tuning can cause instability or poor performance","Network latency and sensor delays can cause control instability; policies trained with zero latency may fail with real-world delays (50-200ms)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"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=octo","compare_url":"https://unfragile.ai/compare?artifact=octo"}},"signature":"0gEgQRECBwdUPCUK9UT32lTk86lFg5yxV56Cn5uDOH5Ufioc+8WfNlj+oFl5QfGcTMnhvA2eOGv2+N/W31OiDA==","signedAt":"2026-06-21T10:16:59.215Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/octo","artifact":"https://unfragile.ai/octo","verify":"https://unfragile.ai/api/v1/verify?slug=octo","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"}}