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Add extensive language support
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Pepijn
@@ -31,8 +31,8 @@
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title: Porting Large Datasets
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- local: using_dataset_tools
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title: Using the Dataset Tools
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- local: dataset_subtask
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title: Using Subtasks in the Dataset
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- local: language_and_recipes
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title: Language Columns and Recipes
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- local: streaming_video_encoding
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title: Streaming Video Encoding
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title: "Datasets"
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@@ -1,277 +0,0 @@
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# Using Subtasks in LeRobot Datasets
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Subtask support in robotics datasets has proven effective in improving robot reasoning and understanding. Subtasks are particularly useful for:
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- **Hierarchical policies**: Building policies that include subtask predictions to visualize robot reasoning in real time
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- **Reward modeling**: Helping reward models understand task progression (e.g., SARM-style stage-aware reward models)
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- **Task decomposition**: Breaking down complex manipulation tasks into atomic, interpretable steps
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LeRobotDataset now supports subtasks as part of its dataset structure, alongside tasks.
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## What are Subtasks?
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While a **task** describes the overall goal (e.g., "Pick up the apple and place it in the basket"), **subtasks** break down the execution into finer-grained steps:
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1. "Approach the apple"
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2. "Grasp the apple"
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3. "Lift the apple"
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4. "Move to basket"
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5. "Release the apple"
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Each frame in the dataset can be annotated with its corresponding subtask, enabling models to learn and predict these intermediate stages.
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<img
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/subtask-asset.png"
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alt="An overview of subtask annotation showing how frames are labeled with intermediate subtask stages"
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width="80%"
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/>
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<p>
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<em>Figure: Overview of subtask annotation.</em>
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</p>
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**Reference:** _Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing_, Original Article, Published: 19 April 2022.
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## Dataset Structure
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Subtask information is stored in the dataset metadata:
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```
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my-dataset/
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├── data/
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│ └── ...
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├── meta/
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│ ├── info.json
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│ ├── stats.json
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│ ├── tasks.parquet
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│ ├── subtasks.parquet # Subtask index → subtask string mapping
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│ └── episodes/
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│ └── ...
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└── videos/
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└── ...
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```
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### Subtasks Parquet File
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The `meta/subtasks.parquet` file maps subtask indices to their natural language descriptions:
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| subtask_index | subtask (index column) |
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| ------------- | ---------------------- |
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| 0 | "Approach the apple" |
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| 1 | "Grasp the apple" |
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| 2 | "Lift the apple" |
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| ... | ... |
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### Frame-Level Annotations
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Each frame in the dataset can include a `subtask_index` field that references the subtasks parquet file:
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```python
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# Example frame data in the parquet file
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{
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"index": 42,
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"timestamp": 1.4,
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"episode_index": 0,
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"task_index": 0,
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"subtask_index": 2, # References "Lift the apple"
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"observation.state": [...],
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"action": [...],
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}
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```
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## Annotating Datasets with Subtasks
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We provide a HuggingFace Space for easily annotating any LeRobotDataset with subtasks:
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**[https://huggingface.co/spaces/lerobot/annotate](https://huggingface.co/spaces/lerobot/annotate)**
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After completing your annotation:
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1. Click "Push to Hub" to upload your annotated dataset
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2. You can also run the annotation space locally by following the instructions at [github.com/huggingface/lerobot-annotate](https://github.com/huggingface/lerobot-annotate)
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## Loading Datasets with Subtasks
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When you load a dataset with subtask annotations, the subtask information is automatically available:
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```python
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from lerobot.datasets import LeRobotDataset
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# Load a dataset with subtask annotations
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dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
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# Access a sample
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sample = dataset[100]
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# The sample includes both task and subtask information
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print(sample["task"]) # "Collect the fruit"
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print(sample["subtask"]) # "Grasp the apple"
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print(sample["task_index"]) # tensor(0)
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print(sample["subtask_index"]) # tensor(2)
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```
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### Checking for Subtask Support
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You can check if a dataset has subtask annotations:
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```python
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# Check if subtasks are available
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has_subtasks = (
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"subtask_index" in dataset.features
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and dataset.meta.subtasks is not None
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)
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if has_subtasks:
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print(f"Dataset has {len(dataset.meta.subtasks)} unique subtasks")
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print("Subtasks:", list(dataset.meta.subtasks.index))
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```
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## Using Subtasks for Training
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### With the Tokenizer Processor
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The `TokenizerProcessor` automatically handles subtask tokenization for Vision-Language Action (VLA) models:
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```python
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from lerobot.processor import TokenizerProcessorStep
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# Create a tokenizer processor step
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tokenizer_processor = TokenizerProcessorStep(
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tokenizer_name_or_path="google/paligemma-3b-pt-224",
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padding="max_length",
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max_length=64,
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)
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# The processor will automatically tokenize subtasks if present in the batch
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# and add them to the observation under:
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# - "observation.subtask.tokens"
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# - "observation.subtask.attention_mask"
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```
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When subtasks are available in the batch, the tokenizer processor adds:
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- `observation.subtask.tokens`: Tokenized subtask text
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- `observation.subtask.attention_mask`: Attention mask for the subtask tokens
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### DataLoader with Subtasks
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```python
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import torch
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from lerobot.datasets import LeRobotDataset
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dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
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dataloader = torch.utils.data.DataLoader(
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dataset,
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batch_size=16,
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shuffle=True,
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)
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for batch in dataloader:
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# Access subtask information in the batch
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subtasks = batch["subtask"] # List of subtask strings
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subtask_indices = batch["subtask_index"] # Tensor of subtask indices
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# Use for training hierarchical policies or reward models
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print(f"Batch subtasks: {set(subtasks)}")
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```
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## Example Datasets with Subtask Annotations
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Try loading a dataset with subtask annotations:
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```python
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from lerobot.datasets import LeRobotDataset
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# Example dataset with subtask annotations
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dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
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# Explore the subtasks
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print("Available subtasks:")
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for subtask_name in dataset.meta.subtasks.index:
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print(f" - {subtask_name}")
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# Get subtask distribution
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subtask_counts = {}
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for i in range(len(dataset)):
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sample = dataset[i]
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subtask = sample["subtask"]
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subtask_counts[subtask] = subtask_counts.get(subtask, 0) + 1
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print("\nSubtask distribution:")
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for subtask, count in sorted(subtask_counts.items(), key=lambda x: -x[1]):
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print(f" {subtask}: {count} frames")
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```
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## Use Cases
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### 1. Hierarchical Policy Training
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Train policies that predict both actions and current subtask:
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```python
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class HierarchicalPolicy(nn.Module):
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def __init__(self, num_subtasks):
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super().__init__()
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self.action_head = nn.Linear(hidden_dim, action_dim)
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self.subtask_head = nn.Linear(hidden_dim, num_subtasks)
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def forward(self, observations):
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features = self.encoder(observations)
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actions = self.action_head(features)
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subtask_logits = self.subtask_head(features)
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return actions, subtask_logits
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```
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### 2. Stage-Aware Reward Modeling (SARM)
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Build reward models that understand task progression:
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```python
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# SARM predicts:
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# - Stage: Which subtask is being executed (discrete)
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# - Progress: How far along the subtask (continuous 0-1)
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class SARMRewardModel(nn.Module):
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def forward(self, observations):
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features = self.encoder(observations)
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stage_logits = self.stage_classifier(features)
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progress = self.progress_regressor(features)
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return stage_logits, progress
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```
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### 3. Progress Visualization
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Monitor robot execution by tracking subtask progression:
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```python
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def visualize_execution(model, observations):
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for t, obs in enumerate(observations):
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action, subtask_logits = model(obs)
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predicted_subtask = subtask_names[subtask_logits.argmax()]
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print(f"t={t}: Executing '{predicted_subtask}'")
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```
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## API Reference
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### LeRobotDataset Properties
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| Property | Type | Description |
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| --------------------------- | ---------------------- | ------------------------------------------ |
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| `meta.subtasks` | `pd.DataFrame \| None` | DataFrame mapping subtask names to indices |
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| `features["subtask_index"]` | `dict` | Feature spec for subtask_index if present |
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### Sample Keys
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When subtasks are available, each sample includes:
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| Key | Type | Description |
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| --------------- | -------------- | ------------------------------------ |
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| `subtask_index` | `torch.Tensor` | Integer index of the current subtask |
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| `subtask` | `str` | Natural language subtask description |
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## Related Resources
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- [SARM Paper](https://arxiv.org/pdf/2509.25358) - Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
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- [LeRobot Annotate Space](https://huggingface.co/spaces/lerobot/annotate) - Interactive annotation tool
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- [LeRobotDataset v3.0](./lerobot-dataset-v3) - Dataset format documentation
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# Language columns and recipes
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LeRobot stores reusable language annotations directly next to frame data in `data/chunk-*/file-*.parquet`.
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The two optional columns are:
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- `language_persistent`: a list of rows broadcast across every frame in an episode for state that remains active, such as `subtask`, `plan`, and `memory`.
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- `language_events`: a list of rows only on the exact frame where an event was emitted, such as `interjection`, `vqa`, and speech tool calls.
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Both columns share the same row shape:
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```text
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role: string
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content: string | null
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style: string | null
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timestamp: float64
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tool_calls: list[Json] | null
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```
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`meta/tasks.parquet` remains the canonical source for the task. The special `${task}` recipe binding always reads that task string and does not depend on language annotations.
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## Architecture
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The language stack has three layers:
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1. `lerobot.datasets.language` defines the schema, style registry, and `column_for_style`.
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2. `lerobot.datasets.language_render` resolves rows and renders messages.
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3. `RenderMessagesStep` turns dataset samples into `messages`, `message_streams`, and `target_message_indices`.
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`LeRobotDataset` stays recipe-agnostic. It passes `language_persistent` and `language_events` through when present, and unannotated datasets keep their existing behavior.
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## Temporal semantics
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Persistent styles are active after emission until replaced:
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- `active_at(t, style=subtask)`
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- `nth_prev(style=memory, offset=1)`
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- `nth_next(style=subtask, offset=1)`
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Event styles only exist on their exact timestamp:
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- `emitted_at(t, style=interjection)`
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- `emitted_at(t, style=vqa, role=user)`
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- `emitted_at(t, role=assistant, tool_name=say)`
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Exact event matching has no tolerance window, so writers must stamp event rows with frame timestamps from the parquet data.
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## Recipe anatomy
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Recipes are YAML files backed by `TrainingRecipe` and `MessageTurn`.
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```yaml
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messages:
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- { role: user, content: "${task}", stream: high_level }
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- { role: assistant, content: "${subtask}", stream: low_level, target: true }
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```
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Rendered samples use HF-style chat messages plus LeRobot sidecars:
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```python
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sample["messages"]
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sample["message_streams"]
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sample["target_message_indices"]
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```
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The renderer does not apply a tokenizer chat template. Policy processors decide how to serialize the messages for their backbone.
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## Blends
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Blend recipes select one weighted sub-recipe deterministically from the sample index.
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The canonical `recipes/pi05_hirobot.yaml` combines memory updates, interjection responses, high-level subtask prediction, low-level execution, and VQA.
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## Graceful absence
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If both language columns are missing, `None`, or empty, `RenderMessagesStep` is a no-op.
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If an event-scoped branch is selected on a frame without the required event row, rendering returns `None`, allowing a loader to retry another sample.
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