{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"rt-2","slug":"rt-2","name":"RT-2","type":"model","url":"https://robotics-transformer2.github.io","page_url":"https://unfragile.ai/rt-2","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"rt-2__cap_0","uri":"capability://planning.reasoning.natural.language.to.robotic.action.translation","name":"natural-language-to-robotic-action-translation","description":"Translates free-form natural language instructions into executable robot control signals by processing robot camera observations alongside text commands through a unified vision-language-action transformer. The model encodes robot actions as text tokens within the language modeling framework, enabling the same transformer architecture to handle both semantic understanding and motor control generation. This co-fine-tuning approach preserves pre-trained vision-language knowledge while adding robotic trajectory supervision, allowing the model to ground language semantics directly to physical actions.","intents":["I want my robot to understand and execute complex natural language commands like 'pick up the red cube and place it next to the blue sphere'","I need a robot to generalize language instructions to novel objects and scenarios not seen during training","I want to avoid hand-coding explicit control policies and instead leverage web-scale language understanding for robot control"],"best_for":["robotics researchers building manipulation systems with natural language interfaces","teams deploying collaborative robots that need to understand human instructions in real-world environments","developers prototyping language-guided robotic applications without extensive domain-specific training data"],"limitations":["Rudimentary reasoning capabilities — not suitable for highly complex multi-step logical reasoning tasks","Specialized for robotic manipulation; applicability to other robot morphologies (locomotion, aerial) unclear from documentation","No explicit handling of temporal reasoning or long-horizon task planning beyond chain-of-thought intermediate steps","Requires robot camera observations as input — no support for other sensor modalities (LiDAR, tactile) mentioned","Action space representation as text tokens may introduce quantization artifacts compared to continuous control outputs"],"requires":["Robot with camera providing real-time visual observations","Access to RT-2 model weights (deployment method and licensing terms unknown)","Inference hardware capable of running transformer-scale vision-language models (GPU VRAM requirements unspecified)","English-language instruction input (support for other languages unknown)"],"input_types":["image (robot camera observation, resolution requirements unspecified)","text (natural language instruction in English)","robot state (format and requirements unspecified)"],"output_types":["text-encoded robot action (specific action space and token format unspecified)","intermediate reasoning steps (when chain-of-thought enabled)"],"categories":["planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_1","uri":"capability://planning.reasoning.semantic.generalization.to.novel.objects","name":"semantic-generalization-to-novel-objects","description":"Leverages pre-trained vision-language model knowledge to recognize and manipulate objects not present in the robot training dataset by grounding language descriptions to visual features learned from internet-scale data. When given an instruction like 'pick up the extinct animal,' the model maps the semantic concept to visual features of novel objects through language understanding rather than explicit object-specific training. This capability emerges from co-fine-tuning robotic trajectories with vision-language tasks, allowing the model to apply learned semantic relationships to new physical scenarios.","intents":["I want my robot to pick up or manipulate objects it has never encountered during training based on semantic descriptions","I need the robot to understand abstract or descriptive object references ('the smallest item', 'something that looks like a tool') without explicit training examples","I want to avoid collecting extensive robotic training data for every possible object the robot might encounter"],"best_for":["robotics teams working in dynamic environments with frequently changing object sets","applications requiring manipulation of novel or custom objects without retraining","research groups studying transfer learning and generalization in embodied AI"],"limitations":["Generalization performance on highly abstract or ambiguous descriptions unknown","No quantitative metrics provided for success rate on novel objects vs. training distribution objects","Semantic understanding limited to visual features — may fail on objects with similar appearance but different semantics","Requires clear visual distinctiveness; performance on occluded or partially visible novel objects unspecified"],"requires":["Pre-trained vision-language model weights (base model architecture unspecified)","Robot camera with sufficient resolution to capture visual features of novel objects","Natural language descriptions that map to learnable visual concepts"],"input_types":["image (robot observation containing novel object)","text (semantic description or instruction referencing the novel object)"],"output_types":["text-encoded robot action targeting the identified novel object"],"categories":["planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_2","uri":"capability://planning.reasoning.comparative.reasoning.over.robot.observations","name":"comparative-reasoning-over-robot-observations","description":"Performs relative comparisons and superlative reasoning on objects in the robot's visual field by leveraging language model understanding of comparative semantics. The model can interpret instructions like 'pick up the smallest object' or 'place it closest to the red cube' by reasoning about spatial and attribute relationships between multiple objects in a single image. This capability combines vision-language understanding with robotic action generation, allowing the model to compute relative properties and select appropriate targets without explicit comparative logic programming.","intents":["I want my robot to understand comparative instructions like 'pick the largest item' or 'move it closer to the target'","I need the robot to reason about spatial relationships and select objects based on relative properties","I want to give instructions that reference multiple objects and their relationships without pre-defining object categories"],"best_for":["robotic manipulation tasks requiring selection among multiple candidate objects","applications with dynamic scenes where object sets change between tasks","teams building natural language interfaces for robot control without explicit scene understanding modules"],"limitations":["Comparative reasoning limited to visual properties visible in single camera frame — no multi-view reasoning mentioned","Performance on ambiguous comparisons (e.g., 'similar size objects') not quantified","No explicit handling of 3D spatial reasoning; comparisons based on 2D image features","Accuracy on complex multi-object scenes with occlusion or clutter unknown"],"requires":["Robot camera observation containing multiple candidate objects","Natural language instruction with comparative or superlative semantics","Pre-trained vision-language model capable of understanding comparative relationships"],"input_types":["image (robot observation with multiple objects)","text (instruction with comparative language: 'smallest', 'closest', 'largest', etc.)"],"output_types":["text-encoded robot action targeting the selected object based on comparison"],"categories":["planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_3","uri":"capability://planning.reasoning.chain.of.thought.multi.stage.reasoning","name":"chain-of-thought-multi-stage-reasoning","description":"Generates intermediate reasoning steps before producing final robot actions, enabling decomposition of complex tasks into semantic sub-goals. When processing instructions like 'use an improvised tool to reach the object,' the model can emit chain-of-thought tokens that reason about available tools, their properties, and applicability before selecting and executing an action. This approach leverages the language model's ability to generate text reasoning steps, then grounds those steps in robotic actions, allowing the model to handle multi-stage semantic reasoning without explicit task decomposition modules.","intents":["I want my robot to reason through complex instructions step-by-step before executing actions","I need the robot to explain its reasoning for action selection in human-readable form","I want to enable more complex task decomposition without explicitly programming sub-goal hierarchies"],"best_for":["applications requiring interpretability and explainability of robot decisions","complex manipulation tasks requiring multi-step semantic reasoning","research on emergent reasoning capabilities in embodied AI systems"],"limitations":["Reasoning capabilities described as 'rudimentary' — not suitable for highly complex logical reasoning","No quantitative evaluation of reasoning quality or accuracy provided","Intermediate reasoning steps may introduce latency compared to direct action generation","No explicit error recovery if intermediate reasoning steps are invalid or contradictory"],"requires":["Complex instruction requiring multi-stage reasoning","Inference system capable of generating and processing intermediate text tokens","Robot capable of executing actions derived from reasoning steps"],"input_types":["image (robot observation)","text (complex natural language instruction)"],"output_types":["text (intermediate reasoning steps)","text-encoded robot action (final action after reasoning)"],"categories":["planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_4","uri":"capability://model.training.co.fine.tuning.with.vision.language.preservation","name":"co-fine-tuning-with-vision-language-preservation","description":"Combines robotic trajectory data with internet-scale vision-language tasks during training while preserving the pre-trained vision-language model's learned representations. Rather than replacing the original model with robot-specific weights, co-fine-tuning maintains the vision and text encoder knowledge while adding robotic action supervision, allowing the model to retain semantic understanding from web-scale data while learning action grounding. This hybrid training approach encodes actions as text tokens to fit into the standard language modeling framework, enabling efficient knowledge transfer from vision-language pretraining to robotic control.","intents":["I want to leverage existing vision-language model knowledge for robot control without losing semantic understanding","I need to train a robot control model with limited robotic data by combining it with internet-scale vision-language supervision","I want to avoid catastrophic forgetting of pre-trained knowledge when fine-tuning on robot-specific tasks"],"best_for":["teams with limited robotic training data looking to leverage pre-trained models","researchers studying transfer learning from vision-language models to embodied AI","organizations wanting to maintain semantic understanding while adding task-specific capabilities"],"limitations":["Co-fine-tuning approach requires careful balancing of robotic and vision-language loss terms — optimal weighting unknown","Scale of robotic training data used in co-fine-tuning not disclosed","No comparison of co-fine-tuning vs. standard fine-tuning provided","Computational cost and training time for co-fine-tuning not specified","Unclear how well this approach generalizes to different robot morphologies or action spaces"],"requires":["Pre-trained vision-language model weights","Robotic trajectory dataset with image observations and action labels","Vision-language task dataset (or access to pre-computed embeddings)","Training infrastructure capable of multi-task learning at scale"],"input_types":["image (robot observation)","text (natural language instruction or vision-language task)","action (robot trajectory data encoded as text tokens)"],"output_types":["trained model weights preserving vision-language knowledge with added action grounding"],"categories":["model-training","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_5","uri":"capability://planning.reasoning.action.as.text.token.representation","name":"action-as-text-token-representation","description":"Encodes robot actions as discrete text tokens within the language model's vocabulary, enabling actions to be generated using the same transformer decoder as natural language. Rather than predicting continuous control values or using separate action heads, the model maps each possible robot action to a unique token, allowing the language modeling framework to handle both semantic understanding and action generation. This unified representation simplifies the architecture and enables joint training on language and robotic tasks without specialized control modules.","intents":["I want to use a standard language model architecture for robot control without adding specialized policy heads","I need to represent robot actions in a way that integrates naturally with language model training","I want to enable joint optimization of language understanding and action generation in a single model"],"best_for":["researchers building unified vision-language-action models","teams wanting to leverage standard transformer architectures for robotics without custom modifications","applications where action discretization is acceptable and action space is limited"],"limitations":["Action discretization may introduce quantization artifacts compared to continuous control outputs","Specific action space size and token mapping not disclosed","Unclear how this approach scales to high-dimensional action spaces or continuous control tasks","No comparison of token-based vs. continuous action representations provided","Action granularity and precision limited by vocabulary size"],"requires":["Discrete action space that can be mapped to a reasonable number of tokens","Robot capable of executing discrete action commands","Training data with actions labeled as discrete tokens"],"input_types":["image (robot observation)","text (natural language instruction)"],"output_types":["text token (representing discrete robot action)"],"categories":["planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_6","uri":"capability://planning.reasoning.vision.language.model.grounding.to.physical.actions","name":"vision-language-model-grounding-to-physical-actions","description":"Grounds abstract semantic concepts from vision-language models to concrete physical robot actions by training on paired robot observations and action trajectories. The model learns to map visual features and language semantics (learned from internet-scale data) to specific motor commands, creating a bridge between high-level semantic understanding and low-level robot control. This grounding process occurs during co-fine-tuning, where robotic trajectory supervision teaches the vision-language model which actions correspond to which visual and linguistic inputs.","intents":["I want to connect high-level semantic understanding from vision-language models to actual robot movements","I need my robot to understand abstract concepts like 'fragile' or 'tool' and translate them to appropriate handling behaviors","I want to leverage semantic knowledge from web-scale data to improve robot control without extensive domain-specific training"],"best_for":["robotics teams building semantic understanding into robot controllers","applications requiring robots to understand abstract or context-dependent instructions","research on grounding language and vision in embodied AI systems"],"limitations":["Grounding quality depends on alignment between vision-language pretraining and robotic task distribution","No quantitative evaluation of grounding accuracy or semantic understanding provided","Unclear how well grounding transfers across different robot morphologies or environments","Potential for semantic drift if robotic training data distribution differs significantly from vision-language pretraining"],"requires":["Pre-trained vision-language model with learned semantic representations","Robotic trajectory dataset with diverse examples of semantic concepts paired with actions","Robot capable of executing the actions in the training data"],"input_types":["image (robot observation with semantic content)","text (natural language instruction with semantic meaning)","action (robot trajectory demonstrating semantic grounding)"],"output_types":["text-encoded robot action grounded in semantic understanding"],"categories":["planning-reasoning","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_7","uri":"capability://data.processing.analysis.6000.trial.robotic.evaluation.framework","name":"6000-trial-robotic-evaluation-framework","description":"Provides evaluation infrastructure for assessing robot control models across 6,000 diverse trials covering different objects, instructions, and scenarios. This evaluation framework enables systematic assessment of generalization, semantic understanding, and action accuracy across a large test set. The scale of evaluation (6,000 trials) suggests comprehensive coverage of task variations, though specific metrics, success criteria, and baseline comparisons are not disclosed in available documentation.","intents":["I want to benchmark my robot control model against a comprehensive evaluation suite","I need to assess generalization performance across diverse objects and instructions","I want to compare my approach against RT-2's evaluation results"],"best_for":["robotics researchers benchmarking vision-language-action models","teams evaluating robot control systems at scale","organizations comparing their approaches against RT-2 baseline"],"limitations":["Specific evaluation metrics and success criteria not disclosed","No breakdown of performance by task category, object type, or instruction complexity","No comparison against baselines or prior work (e.g., RT-1) provided","Evaluation environment and robot hardware specifications unknown","No public access to evaluation dataset or benchmark results mentioned"],"requires":["Robot capable of executing manipulation tasks","Evaluation environment with diverse objects and scenarios","Ability to measure task success (e.g., object placement accuracy)"],"input_types":["image (robot observation)","text (natural language instruction)","ground truth (expected action or success criteria)"],"output_types":["evaluation metrics (success rate, accuracy, etc. — specific metrics unknown)"],"categories":["data-processing-analysis","robotics-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_8","uri":"capability://planning.reasoning.visual.grounding.of.natural.language.instructions.to.robot.observations","name":"visual grounding of natural language instructions to robot observations","description":"RT-2 grounds natural language instructions to specific visual elements in robot observations by jointly processing images and text through the vision-language transformer. When given an instruction like 'pick up the red cube,' the model identifies the red cube in the visual scene and predicts actions to manipulate it — this grounding emerges from the transformer's ability to attend to relevant visual regions while processing language. The model learns to align language tokens with visual features through co-training on vision-language tasks.","intents":["I want my robot to understand which objects or regions in the visual scene correspond to natural language descriptions","I need my robot to follow instructions that reference specific visual elements without explicit object detection","I want my robot to ground abstract language concepts to concrete visual observations"],"best_for":["manipulation tasks requiring precise visual grounding of language instructions","research on vision-language grounding in embodied AI","scenarios where explicit object detection or segmentation are infeasible"],"limitations":["Grounding accuracy and robustness not quantified — unclear how reliably the model grounds language to visual elements","Failure modes for ambiguous or under-specified language descriptions not documented","No explicit mechanism for handling multiple candidate visual elements matching a description","Grounding may fail for visual elements with unusual appearance or occlusion"],"requires":["Training data with language-annotated robot observations","Pre-training on vision-language tasks requiring grounding (e.g., VQA, visual reasoning)","Clear visual observations with distinct, identifiable objects or regions"],"input_types":["text (natural language instruction with object or region references)","image (robot observation)"],"output_types":["text tokens (action targeting grounded visual element)","implicit visual grounding (not explicitly returned)"],"categories":["planning-reasoning","robotics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__cap_9","uri":"capability://planning.reasoning.evaluation.and.benchmarking.on.6000.robotic.manipulation.trials","name":"evaluation and benchmarking on 6000+ robotic manipulation trials","description":"RT-2 was evaluated on 6,000+ robotic manipulation trials to assess performance on object picking, generalization to novel objects, out-of-distribution command interpretation, and comparative reasoning tasks. The evaluation protocol tests the model's ability to follow natural language instructions in real robotic scenarios, though specific quantitative metrics, success rates, and comparison to baselines are not publicly documented. The evaluation scale demonstrates the feasibility of the approach but lacks detailed performance characterization.","intents":["I want to understand how well RT-2 performs on real robotic manipulation tasks","I need quantitative metrics on success rates, generalization, and robustness","I want to compare RT-2's performance to alternative approaches or baselines"],"best_for":["researchers evaluating vision-language-action models for robotics","teams assessing whether RT-2 is suitable for their specific robotic tasks","organizations benchmarking robot learning approaches"],"limitations":["Specific quantitative metrics (success rates, accuracy, latency) not publicly documented","No comparison to baselines or alternative approaches provided","Evaluation limited to manipulation tasks — generalization to other robot morphologies or tasks unknown","Robot platforms and specific evaluation scenarios not detailed","No analysis of failure modes or edge cases","Evaluation data and benchmarks not publicly released for independent verification"],"requires":["Access to robotic manipulation platforms for evaluation","Natural language-annotated task descriptions","Metrics for assessing task success (e.g., object successfully picked, placed correctly)"],"input_types":["robot observations (images from manipulation trials)","natural language task instructions"],"output_types":["task success/failure labels","performance metrics (not documented)"],"categories":["planning-reasoning","robotics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rt-2__headline","uri":"capability://model.training.vision.language.action.model.for.robotics","name":"vision-language-action model for robotics","description":"RT-2 is a cutting-edge vision-language-action model that enables robots to understand and execute complex natural language instructions by leveraging web-scale knowledge for robotic control.","intents":["best vision-language-action model","vision-language model for robotics","robot control using natural language","how to make robots understand commands","top models for robotic instruction execution"],"best_for":["robotics applications","natural language processing in robotics"],"limitations":["may struggle with unseen commands"],"requires":["input images","natural language commands"],"input_types":["images","text"],"output_types":["robot actions"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["Robot with camera providing real-time visual observations","Access to RT-2 model weights (deployment method and licensing terms unknown)","Inference hardware capable of running transformer-scale vision-language models (GPU VRAM requirements unspecified)","English-language instruction input (support for other languages unknown)","Pre-trained vision-language model weights (base model architecture unspecified)","Robot camera with sufficient resolution to capture visual features of novel objects","Natural language descriptions that map to learnable visual concepts","Robot camera observation containing multiple candidate objects","Natural language instruction with comparative or superlative semantics","Pre-trained vision-language model capable of understanding comparative relationships"],"failure_modes":["Rudimentary reasoning capabilities — not suitable for highly complex multi-step logical reasoning tasks","Specialized for robotic manipulation; applicability to other robot morphologies (locomotion, aerial) unclear from documentation","No explicit handling of temporal reasoning or long-horizon task planning beyond chain-of-thought intermediate steps","Requires robot camera observations as input — no support for other sensor modalities (LiDAR, tactile) mentioned","Action space representation as text tokens may introduce quantization artifacts compared to continuous control outputs","Generalization performance on highly abstract or ambiguous descriptions unknown","No quantitative metrics provided for success rate on novel objects vs. training distribution objects","Semantic understanding limited to visual features — may fail on objects with similar appearance but different semantics","Requires clear visual distinctiveness; performance on occluded or partially visible novel objects unspecified","Comparative reasoning limited to visual properties visible in single camera frame — no multi-view reasoning mentioned","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"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":"active","updated_at":"2026-05-24T12:16:25.061Z","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=rt-2","compare_url":"https://unfragile.ai/compare?artifact=rt-2"}},"signature":"YlbNlJn1VXLWIfdMdfQnwQT8vicCU4TgC/p+FDVdgoZrterk5yvp/u4CVGFukNApQ21sF5Fvf3qG+Ml+J4BiBQ==","signedAt":"2026-06-19T17:59:54.113Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rt-2","artifact":"https://unfragile.ai/rt-2","verify":"https://unfragile.ai/api/v1/verify?slug=rt-2","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"}}