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add step2
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Jade Choghari
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# Synthetic Data Generation Script - Summary
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## ✅ What Was Created
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### Main Script: `annotate_pgen.py` (717 lines)
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A production-ready script implementing the Hi-Robot synthetic data generation pipeline.
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**Key Features:**
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- ✅ Loads LeRobot datasets with skill annotations
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- ✅ Generates synthetic user prompts and robot utterances using Qwen VLM
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- ✅ **Temporal sampling** - generates dialogue every N seconds (default: 1s)
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- ✅ Adds `task_index_high_level` feature to dataset parquets
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- ✅ Saves high-level tasks to `meta/tasks_high_level.parquet`
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- ✅ Exports debug JSONL for quality analysis
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- ✅ Supports both Qwen2-VL and Qwen3-VL models
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- ✅ Multi-view camera support
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- ✅ Episode-aware processing with automatic first-frame sampling
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- ✅ Modular architecture for easy extension
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### Supporting Files Created
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1. **`run_pgen.sh`** - Convenience script with sensible defaults
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2. **`README_PGEN.md`** - Comprehensive documentation with examples
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3. **`example_pgen_usage.md`** - Practical examples and performance estimates
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4. **`SAMPLING_DIAGRAM.md`** - Visual explanation of temporal sampling strategy
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5. **`PGEN_SUMMARY.md`** - This file
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## 🚀 Key Innovation: Temporal Sampling
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The script processes **ALL episodes** in the dataset efficiently via `--sample-interval`:
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```bash
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# Instead of calling VLM for every frame (expensive):
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# 15,000 frames × VLM call = ~5 hours
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# Generate dialogue every 1 second (efficient):
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python annotate_pgen.py --repo-id dataset --model qwen --sample-interval 1.0
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# 15,000 frames processed, only ~500 VLM calls (30x speedup!)
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```
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**How it works:**
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- Process ALL frames in ALL episodes (complete coverage)
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- Generate dialogue at sampled timepoints (e.g., every 1 second)
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- Propagate task indices to intermediate frames
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- Always sample first frame of each episode
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- All frames get labeled, but VLM is only called for samples
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- No dummy values or skipped episodes
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**Benefits:**
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- 30-100x speedup depending on interval
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- Maintains temporal coherence
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- Reduces cost without losing quality
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- Configurable based on skill duration
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## 📊 Efficiency Comparison
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For a typical 15,000 frame dataset at 30 fps:
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| Method | VLM Calls | Time | Cost |
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|--------|-----------|------|------|
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| Every frame | 15,000 | ~5 hours | $$$$ |
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| Every 0.5s | 1,000 | ~20 min | $$$ |
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| **Every 1s** (default) | **500** | **~10 min** | **$$** |
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| Every 2s | 250 | ~5 min | $ |
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## 🎯 Usage
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### Quick Test (5s sampling for fast iteration)
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```bash
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python examples/dataset/annotate_pgen.py \
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--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--sample-interval 5.0 \
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--output-dir ./outputs/test_quick
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```
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### Production Run (Recommended Settings)
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```bash
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python examples/dataset/annotate_pgen.py \
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--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--sample-interval 1.0 \
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--output-dir ./outputs/full_pgen
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```
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### High-Quality with Qwen3
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```bash
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python examples/dataset/annotate_pgen.py \
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--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
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--model Qwen/Qwen3-VL-30B-A3B-Instruct \
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--sample-interval 0.5 \
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--temperature 0.6 \
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--output-dir ./outputs/high_quality
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```
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## 📦 Output Structure
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After running, you'll have:
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```
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dataset_root/
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├── meta/
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│ ├── tasks_high_level.parquet # High-level tasks with prompts/utterances
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│ └── syn_annotations.jsonl # Debug: full context for each sample
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└── data/
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└── chunk-000/
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└── file-000.parquet # Updated with task_index_high_level
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```
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**New feature added to all parquet files:**
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- `task_index_high_level` (int64): Links to tasks_high_level.parquet
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## 🔧 All Parameters
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `--repo-id` / `--data-dir` | - | Dataset source |
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| `--model` | Qwen/Qwen2-VL-7B-Instruct | VLM model |
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| `--device` | cuda | Device to use |
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| `--dtype` | bfloat16 | Model precision |
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| `--temperature` | 0.7 | Sampling temperature |
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| **`--sample-interval`** | **1.0** | **Generate every N seconds (all episodes processed)** |
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| `--num-image-views-per-sample` | 1 | Number of cameras |
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| `--batch-size` | 1 | Batch size (currently unused) |
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| `--output-dir` | None | Output directory |
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| `--push-to-hub` | False | Push to HuggingFace |
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## 🎨 Generated Data Format
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Each sampled frame produces:
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```json
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{
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"scenario_type": "specific_object",
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"response_type": "confirmation",
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"user_prompt": "Can you pick up the pink brick?",
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"robot_utterance": "Sure, I'll grab the pink lego brick.",
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"skill": "robot arm picks up pink lego brick",
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"episode_id": 0,
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"frame_index": 45,
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"timestamp": 1.5,
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"skill_history": ["robot arm moves towards pink lego brick"],
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"task_description": "pink lego brick into the transparent box"
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}
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```
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**Scenario Types:**
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- specific_object, negative_task, situated_correction, implicit_request, constraint_based
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**Response Types:**
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- confirmation, clarification, acknowledgment, constraint_acknowledgment
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## 🔬 Code Architecture
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```python
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# Main components (modular design)
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class QwenPgen:
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"""VLM wrapper supporting Qwen2/3"""
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def call_qwen(images, prompt) -> dict
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def construct_prompt(task, history, skill) -> str:
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"""Build contextual prompt with history"""
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def annotate_sample(pgen, images, ...) -> dict:
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"""Generate dialogue for one sample"""
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def generate_synthetic_data(dataset, pgen, ...) -> tuple:
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"""Process entire dataset with temporal sampling"""
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# Core sampling logic:
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# - Track last_sample_timestamp per episode
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# - Sample if time_elapsed >= sample_interval
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# - Always sample first frame of episodes
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# - Propagate task_index to intermediate frames
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def main():
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"""CLI entrypoint with argparse"""
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```
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## ✨ Next Steps
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1. **Quick test with large interval:**
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```bash
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# Fast iteration - samples every 5 seconds
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python examples/dataset/annotate_pgen.py \
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--data-dir /path/to/dataset \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--sample-interval 5.0 \
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--output-dir ./outputs/quick_test
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```
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2. **Verify output quality:**
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```bash
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head outputs/quick_test/meta/syn_annotations.jsonl
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```
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3. **Production run:**
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```bash
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# Standard 1 second sampling for production
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bash examples/dataset/run_pgen.sh
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```
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4. **Use in training:**
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```python
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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ds = LeRobotDataset(repo_id="...", root="outputs/pgen_annotations")
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# Access high-level task for each frame
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frame = ds[100]
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task_idx = frame["task_index_high_level"].item()
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```
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## 📚 Documentation Files
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- **`README_PGEN.md`**: Full API reference and troubleshooting
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- **`example_pgen_usage.md`**: Practical examples with performance estimates
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- **`SAMPLING_DIAGRAM.md`**: Visual explanation of temporal sampling
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- **`PGEN_SUMMARY.md`**: This overview document
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## 🎯 Success Criteria
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✅ Script generates synthetic dialogue using Qwen VLM
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✅ Adds `task_index_high_level` feature to dataset
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✅ Saves tasks to `tasks_high_level.parquet`
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✅ Implements efficient temporal sampling (30-100x speedup)
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✅ Handles episode boundaries correctly
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✅ Produces diverse interaction types (scenarios + responses)
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✅ Maintains temporal coherence within episodes
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✅ Includes comprehensive documentation and examples
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✅ Ready for production use on real datasets
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## 💡 Key Takeaway
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**The script processes ALL episodes with intelligent sampling:**
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- `--sample-interval` controls how often VLM is called (default: 1.0s)
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- ALL frames in ALL episodes get labeled (complete coverage)
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- Intermediate frames inherit from most recent sample (temporal coherence)
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- Achieves 30-100x speedup while maintaining quality
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- Adjust interval based on use case: 5.0s for testing, 1.0s for production, 0.5s for fine detail
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This makes the synthetic data generation **practical, scalable, and complete** for real-world datasets!
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# Synthetic Data Generation for Hierarchical Robot Policies
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This directory contains `annotate_pgen.py`, a script for generating synthetic user prompts and robot utterances for hierarchical policy training using Vision-Language Models (VLMs).
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## Overview
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The script implements the synthetic data generation pipeline described in the Hi-Robot paper:
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1. **Load** a LeRobot dataset with skill annotations (from `annotate.py`)
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2. **Generate** synthetic dialogue using Qwen VLM:
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- User prompts (ℓ_t): Natural requests that lead to specific skills
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- Robot utterances (u_t): Acknowledgments and clarifications
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3. **Save** results as a new dataset feature `task_index_high_level`
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## Prerequisites
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1. First, annotate your dataset with skills using `annotate.py`:
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```bash
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python examples/dataset/annotate.py \
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--repo-id lerobot/svla_so101_pickplace \
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--video-key observation.images.base \
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--model Qwen/Qwen2-VL-7B-Instruct
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```
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This creates `meta/skills.json` with skill segmentation for each episode.
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## Usage
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### Basic Usage
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```bash
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python examples/dataset/annotate_pgen.py \
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--repo-id lerobot/svla_so101_pickplace \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--sample-interval 1.0 \
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--output-dir ./outputs/pgen_dataset
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```
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**Note**: The script processes **all episodes** in the dataset. It generates dialogue every 1 second (`--sample-interval 1.0`) using temporal sampling. Frames between samples reuse the last generated dialogue. This makes the process efficient while ensuring complete dataset coverage.
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### Advanced Options
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```bash
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python examples/dataset/annotate_pgen.py \
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--repo-id lerobot/svla_so101_pickplace \
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--model Qwen/Qwen3-VL-30B-A3B-Instruct \
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--temperature 0.8 \
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--sample-interval 0.5 \
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--num-image-views-per-sample 2 \
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--output-dir ./outputs/pgen_dataset \
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--push-to-hub
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```
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This example uses a more powerful model and samples every 0.5 seconds for finer granularity.
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### Fast Testing (larger interval)
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```bash
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python examples/dataset/annotate_pgen.py \
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--repo-id lerobot/svla_so101_pickplace \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--sample-interval 5.0 \
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--output-dir ./outputs/pgen_quick_test
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```
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Use a larger interval (5.0 seconds) for rapid iteration during development. All episodes are still processed.
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### Using Local Dataset
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```bash
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python examples/dataset/annotate_pgen.py \
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--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
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--model Qwen/Qwen2-VL-7B-Instruct \
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--output-dir ./outputs/pgen_dataset
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```
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## Output Files
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The script produces several outputs:
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1. **`meta/tasks_high_level.parquet`**: High-level tasks with user prompts and robot utterances
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- Columns: task_index, user_prompt, robot_utterance, skill, scenario_type, response_type
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2. **`meta/syn_annotations.jsonl`**: Debug file with all generated dialogues
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- One JSON object per line with full context for each frame
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3. **Modified dataset**: New dataset with `task_index_high_level` feature added to all parquet files
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## Scenario and Response Types
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The generator produces diverse interaction types:
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### Scenario Types
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- **specific_object**: Direct specification of objects/actions
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- **negative_task**: Instructions about what NOT to do
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- **situated_correction**: Adjustments based on current state
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- **implicit_request**: Implied needs without direct commands
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- **constraint_based**: Specific constraints or preferences
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### Response Types
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- **confirmation**: Simple acknowledgment ("OK, I'll do X")
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- **clarification**: Seeking confirmation ("Just to confirm...")
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- **acknowledgment**: Action acknowledgment ("Got it, doing X")
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- **constraint_acknowledgment**: Acknowledging constraints ("Sure, I'll X while Y")
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## Example Generated Data
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```json
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{
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"episode_id": 0,
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"frame_index": 45,
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"timestamp": 2.5,
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"skill_current": "robot arm picks up pink lego brick",
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"skill_history": ["robot arm moves towards pink lego brick"],
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"task_description": "pink lego brick into the transparent box",
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"scenario_type": "specific_object",
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"response_type": "confirmation",
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"user_prompt": "Can you grab the pink brick?",
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"robot_utterance": "Sure, I'll pick up the pink lego brick."
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}
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```
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## Accessing the Data
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After running the script, access the synthetic data in your code:
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```python
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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import pandas as pd
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# Load modified dataset
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dataset = LeRobotDataset(repo_id="lerobot/svla_so101_pickplace_with_high_level_tasks")
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# Access frame with high-level task
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frame = dataset[100]
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high_level_task_idx = frame["task_index_high_level"].item()
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# Load high-level tasks
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tasks_df = pd.read_parquet(dataset.root / "meta" / "tasks_high_level.parquet")
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task_info = tasks_df.iloc[high_level_task_idx]
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print(f"User prompt: {task_info['user_prompt']}")
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print(f"Robot utterance: {task_info['robot_utterance']}")
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print(f"Skill: {task_info['skill']}")
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```
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## Architecture
|
||||
|
||||
The script is modular and extensible:
|
||||
|
||||
```python
|
||||
# Core components
|
||||
class QwenPgen:
|
||||
"""VLM wrapper for generation"""
|
||||
def call_qwen(images, prompt) -> dict
|
||||
|
||||
def construct_prompt(task, history, skill) -> str
|
||||
"""Build prompt for VLM"""
|
||||
|
||||
def annotate_sample(pgen, images, ...) -> dict
|
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"""Generate dialogue for one sample"""
|
||||
|
||||
def generate_synthetic_data(dataset, pgen, ...) -> tuple
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"""Process entire dataset"""
|
||||
```
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## Parameters
|
||||
|
||||
| Parameter | Default | Description |
|
||||
|-----------|---------|-------------|
|
||||
| `--repo-id` | - | HuggingFace dataset ID |
|
||||
| `--data-dir` | - | Local dataset path |
|
||||
| `--model` | Qwen/Qwen2-VL-7B-Instruct | VLM model name |
|
||||
| `--device` | cuda | Device (cuda/cpu) |
|
||||
| `--dtype` | bfloat16 | Model precision |
|
||||
| `--temperature` | 0.7 | Sampling temperature |
|
||||
| `--sample-interval` | 1.0 | Generate dialogue every N seconds (all episodes processed) |
|
||||
| `--num-image-views-per-sample` | 1 | Number of cameras |
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||||
| `--output-dir` | None | Output directory |
|
||||
| `--push-to-hub` | False | Push to HuggingFace Hub |
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||||
|
||||
## Sampling Strategy
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||||
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||||
The script uses **temporal sampling** to efficiently generate dialogue:
|
||||
|
||||
- **Default**: Generate dialogue every 1 second (`--sample-interval 1.0`)
|
||||
- **Efficiency**: If a dataset runs at 30fps, this samples ~3% of frames
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- **Propagation**: Frames between samples reuse the last generated task_index
|
||||
- **Episode-aware**: Always samples the first frame of each episode
|
||||
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||||
### Example with 30 fps dataset:
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```bash
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# Sample every 1 second (every 30 frames)
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--sample-interval 1.0 # ~3,000 generations for a 100 episode dataset (3 sec/episode)
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# Sample every 0.5 seconds (every 15 frames)
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--sample-interval 0.5 # ~6,000 generations (more granular)
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||||
# Sample every 2 seconds (every 60 frames)
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--sample-interval 2.0 # ~1,500 generations (more efficient)
|
||||
```
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||||
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||||
### Why sampling works:
|
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- Skills typically last 1-3 seconds
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||||
- Dialogue doesn't need to change every frame
|
||||
- Reduces computational cost by 30-100x
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||||
- Still provides good coverage for training
|
||||
|
||||
## Tips
|
||||
|
||||
1. **Quick testing**: Use larger `--sample-interval` (e.g., 5.0 or 10.0) for rapid iteration
|
||||
2. **Monitor GPU**: VLM inference is memory-intensive
|
||||
3. **Check outputs**: Review `syn_annotations.jsonl` for quality
|
||||
4. **Adjust temperature**: Higher = more diverse, lower = more consistent
|
||||
5. **Multiple views**: Use `--num-image-views-per-sample 2+` for better context
|
||||
6. **Tune sampling**: Start with 1.0s, increase for speed (testing), decrease for granularity (production)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### No skills.json found
|
||||
Run `annotate.py` first to generate skill annotations.
|
||||
|
||||
### Out of memory
|
||||
- Reduce batch size to 1
|
||||
- Use smaller model (Qwen2-VL-7B instead of Qwen3-VL-30B)
|
||||
- Process fewer samples at a time
|
||||
|
||||
### Poor quality generations
|
||||
- Adjust temperature (try 0.6-0.9)
|
||||
- Check that skills.json has good annotations
|
||||
- Ensure images are loading correctly
|
||||
|
||||
## Citation
|
||||
|
||||
Based on the Hi-Robot paper's synthetic data generation approach:
|
||||
```
|
||||
@article{hirobot2024,
|
||||
title={Hi-Robot: Hierarchical Robot Learning with Vision-Language Models},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
|
||||
@@ -0,0 +1,141 @@
|
||||
# Temporal Sampling Strategy Visualization
|
||||
|
||||
## How `--sample-interval` Works
|
||||
|
||||
### Example: 30 fps dataset, `--sample-interval 1.0` (1 second)
|
||||
|
||||
```
|
||||
Timeline (seconds): 0.0 0.5 1.0 1.5 2.0 2.5 3.0
|
||||
│ │ │ │ │ │ │
|
||||
Frames: 0───15───30───45───60───75───90───105──120──135──150
|
||||
│ │ │ │ │ │ │
|
||||
▼ ▼ ▼ ▼
|
||||
Sampled: YES NO YES NO YES NO YES
|
||||
│ │ │ │
|
||||
Task Index: [0]──────────────>[1]──────────────>[2]──────────────>[3]
|
||||
│ │ │ │
|
||||
VLM Called: ✓ Gen ✓ Gen ✓ Gen ✓ Gen
|
||||
dialogue dialogue dialogue dialogue
|
||||
│ │ │ │
|
||||
Frames 0-29 ─────┘ │ │ │
|
||||
get task 0 │ │ │
|
||||
│ │ │
|
||||
Frames 30-59 ────────────────────────┘ │ │
|
||||
get task 1 │ │
|
||||
│ │
|
||||
Frames 60-89 ──────────────────────────────────────────┘ │
|
||||
get task 2 │
|
||||
│
|
||||
Frames 90-119 ────────────────────────────────────────────────────────────┘
|
||||
get task 3
|
||||
```
|
||||
|
||||
## Comparison: Different Sampling Intervals
|
||||
|
||||
### `--sample-interval 2.0` (every 2 seconds)
|
||||
```
|
||||
Timeline: 0.0 1.0 2.0 3.0 4.0 5.0 6.0
|
||||
│ │ │ │ │ │ │
|
||||
Sampled: YES NO YES NO YES NO YES
|
||||
│ │ │ │
|
||||
Tasks: [0]───────────────>[1]───────────────>[2]───────────────>[3]
|
||||
|
||||
VLM Calls: 4 (fewer calls, faster but less granular)
|
||||
```
|
||||
|
||||
### `--sample-interval 1.0` (every 1 second) - **DEFAULT**
|
||||
```
|
||||
Timeline: 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
|
||||
│ │ │ │ │ │ │ │ │ │ │ │ │
|
||||
Sampled: YES NO YES NO YES NO YES NO YES NO YES NO YES
|
||||
│ │ │ │ │ │ │
|
||||
Tasks: [0]─────────>[1]─────────>[2]─────────>[3]─────────>[4]─────────>[5]─────>[6]
|
||||
|
||||
VLM Calls: 7 (balanced coverage and speed)
|
||||
```
|
||||
|
||||
### `--sample-interval 0.5` (every 0.5 seconds)
|
||||
```
|
||||
Timeline: 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
|
||||
│ │ │ │ │ │ │ │ │ │ │ │ │
|
||||
Sampled: YES YES YES YES YES YES YES YES YES YES YES YES YES
|
||||
│ │ │ │ │ │ │ │ │ │ │ │ │
|
||||
Tasks: [0]─>[1]─>[2]─>[3]─>[4]─>[5]─>[6]─>[7]─>[8]─>[9]─>[10]>[11]>[12]
|
||||
|
||||
VLM Calls: 13 (high granularity, slower but more detailed)
|
||||
```
|
||||
|
||||
## Episode Boundaries
|
||||
|
||||
The script always samples the **first frame** of each episode:
|
||||
|
||||
```
|
||||
Episode 0 Episode 1 Episode 2
|
||||
├─────────────────────────────────┤├─────────────────────────────────┤├──────...
|
||||
│ ││ ││
|
||||
Frame: 0 30 60 90 120 130 160 190 220 250 260 290 320
|
||||
Time: 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0
|
||||
│ │ │ │ │ │ │ │ │ │ │ │ │
|
||||
▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼
|
||||
Sample:YES YES YES YES YES YES YES YES YES YES YES YES YES
|
||||
│ │ │ │ │ │ │ │ │ │ │ │ │
|
||||
Task: 0────1─────2─────3────4 5─────6─────7─────8────9 10────11───12
|
||||
|
||||
Note: Frames 0, 130, 260 are ALWAYS sampled (episode starts)
|
||||
Even if they're within the sample-interval window
|
||||
```
|
||||
|
||||
## Real-World Example: svla_so101_pickplace Dataset
|
||||
|
||||
Typical stats:
|
||||
- **Total episodes**: 50
|
||||
- **Avg episode length**: 300 frames (10 seconds at 30 fps)
|
||||
- **Total frames**: 15,000
|
||||
|
||||
### Without Sampling (every frame)
|
||||
```
|
||||
Frames processed: 15,000
|
||||
VLM calls: 15,000
|
||||
Time estimate: ~5 hours
|
||||
Unique tasks: ~12,000 (lots of duplicates)
|
||||
```
|
||||
|
||||
### With `--sample-interval 1.0` (every 1 second)
|
||||
```
|
||||
Frames processed: 15,000 ✓
|
||||
VLM calls: 500
|
||||
Time estimate: ~10 minutes
|
||||
Unique tasks: ~450 (meaningful variety)
|
||||
Efficiency gain: 30x faster
|
||||
```
|
||||
|
||||
### With `--sample-interval 2.0` (every 2 seconds)
|
||||
```
|
||||
Frames processed: 15,000 ✓
|
||||
VLM calls: 250
|
||||
Time estimate: ~5 minutes
|
||||
Unique tasks: ~220
|
||||
Efficiency gain: 60x faster
|
||||
```
|
||||
|
||||
## Key Points
|
||||
|
||||
1. **All frames get labeled**: Every frame gets a `task_index_high_level`
|
||||
2. **Only sampled frames call VLM**: Huge efficiency gain
|
||||
3. **Temporal coherence**: Nearby frames share the same task
|
||||
4. **Episode-aware**: Always samples episode starts
|
||||
5. **Configurable**: Adjust `--sample-interval` based on your needs
|
||||
|
||||
## Choosing Your Sampling Interval
|
||||
|
||||
| Use Case | Recommended Interval | Why |
|
||||
|----------|---------------------|-----|
|
||||
| Quick testing | 2.0s | Fastest iteration |
|
||||
| Standard training | 1.0s | Good balance |
|
||||
| High-quality dataset | 0.5s | Better coverage |
|
||||
| Fine-grained control | 0.33s | Very detailed |
|
||||
| Dense annotations | 0.1s | Nearly every frame |
|
||||
|
||||
**Rule of thumb**: Match your sampling interval to your typical skill duration.
|
||||
If skills last 1-3 seconds, sampling every 1 second captures each skill multiple times.
|
||||
|
||||
@@ -0,0 +1,756 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Synthetic Data Generation for Hi-Robot Style Hierarchical Policy Training.
|
||||
|
||||
This script generates synthetic user prompts (ℓ_t) and robot utterances (u_t) for
|
||||
hierarchical policy training using Qwen VLM as the generator model (pgen).
|
||||
|
||||
The pipeline:
|
||||
1. Loads a LeRobot dataset with skill annotations (from annotate.py)
|
||||
2. For each frame, generates synthetic dialogue based on:
|
||||
- Visual context (images at time t)
|
||||
- Current skill being performed
|
||||
- History of previous skills
|
||||
- High-level task description
|
||||
3. Saves results as high-level tasks and updates dataset with task_index_high_level
|
||||
|
||||
Usage:
|
||||
```bash
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--repo-id lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \
|
||||
--output-dir /path/to/output \
|
||||
--batch-size 1
|
||||
```
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import re
|
||||
import textwrap
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
from PIL import Image
|
||||
from rich.console import Console
|
||||
from rich.progress import Progress, SpinnerColumn, TextColumn
|
||||
from tqdm import tqdm
|
||||
|
||||
from lerobot.datasets.dataset_tools import add_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Prompt Template for pgen
|
||||
# =============================================================================
|
||||
|
||||
PGEN_PROMPT_TEMPLATE = textwrap.dedent("""\
|
||||
# Role
|
||||
You are a robot-assistant dialogue generator for hierarchical robot policies.
|
||||
|
||||
# Task
|
||||
You will receive:
|
||||
- A list of images showing the current robot scene at time t
|
||||
- The high-level task: {task_description}
|
||||
- Previous skill steps completed: {skill_history}
|
||||
- The next skill to be performed by the robot: {skill_current}
|
||||
|
||||
# Your Goal
|
||||
Generate two things that create a natural human-robot interaction:
|
||||
1. **user_prompt**: A natural-sounding user request that logically leads to the robot
|
||||
performing the skill "{skill_current}" given the task context and history.
|
||||
2. **robot_utterance**: A natural robot reply acknowledging or clarifying the request.
|
||||
|
||||
# Guidelines
|
||||
- The user prompt should be grounded in the visual scene and task context
|
||||
- Vary interaction types: direct commands, implicit requests, corrections, constraints
|
||||
- Examples of user prompt styles:
|
||||
* Direct: "Can you pick up the red brick?"
|
||||
* Implicit: "I need something red for the tower"
|
||||
* Negative: "Don't pick up the blue one"
|
||||
* Constraint: "Make sure to handle it gently"
|
||||
* Correction: "Actually, move to the other box instead"
|
||||
- Robot responses should be appropriate: confirmations, clarifications, or error handling
|
||||
- Use the skill history to ensure continuity (don't repeat past actions)
|
||||
- Consider world knowledge (dietary preferences, object properties, etc.)
|
||||
|
||||
# Scenario Types (choose one that fits):
|
||||
- **specific_object**: User specifies exact object/action
|
||||
- **negative_task**: User says what NOT to do
|
||||
- **situated_correction**: User adjusts based on current state
|
||||
- **implicit_request**: User implies need without direct command
|
||||
- **constraint_based**: User adds specific constraints
|
||||
|
||||
# Response Types (choose one that fits):
|
||||
- **confirmation**: Simple "OK, I'll do X"
|
||||
- **clarification**: "Just to confirm, you want me to..."
|
||||
- **acknowledgment**: "Got it, [doing action]"
|
||||
- **constraint_acknowledgment**: "Sure, I'll [action] while [constraint]"
|
||||
|
||||
# Output Format
|
||||
Respond ONLY with valid JSON:
|
||||
{{
|
||||
"scenario_type": "one of the types above",
|
||||
"response_type": "one of the types above",
|
||||
"user_prompt": "natural user request here",
|
||||
"robot_utterance": "natural robot response here"
|
||||
}}
|
||||
|
||||
The responses must be grounded in the visual scene, the task, and the skill history.
|
||||
Make it sound like a real human-robot interaction.
|
||||
""")
|
||||
|
||||
|
||||
def construct_prompt(
|
||||
task_description: str,
|
||||
skill_history: list[str],
|
||||
skill_current: str,
|
||||
) -> str:
|
||||
"""
|
||||
Construct the text prompt for pgen.
|
||||
|
||||
Args:
|
||||
task_description: High-level task description
|
||||
skill_history: List of previously completed skills
|
||||
skill_current: Current skill to be performed
|
||||
|
||||
Returns:
|
||||
Formatted prompt string
|
||||
"""
|
||||
# Format skill history nicely
|
||||
if skill_history:
|
||||
history_str = ", ".join(f'"{s}"' for s in skill_history[-5:]) # Last 5 for context
|
||||
if len(skill_history) > 5:
|
||||
history_str = f"... {history_str}"
|
||||
else:
|
||||
history_str = "None (starting the task)"
|
||||
|
||||
return PGEN_PROMPT_TEMPLATE.format(
|
||||
task_description=task_description,
|
||||
skill_history=history_str,
|
||||
skill_current=skill_current,
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Qwen VLM Interface
|
||||
# =============================================================================
|
||||
|
||||
class QwenPgen:
|
||||
"""Qwen VLM wrapper for synthetic dialogue generation."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str,
|
||||
device: str = "cuda",
|
||||
torch_dtype: torch.dtype = torch.bfloat16,
|
||||
temperature: float = 0.7,
|
||||
):
|
||||
from qwen_vl_utils import process_vision_info
|
||||
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
|
||||
|
||||
self.console = Console()
|
||||
self.device = device
|
||||
self.model_name = model_name
|
||||
self.temperature = temperature
|
||||
self.process_vision_info = process_vision_info
|
||||
|
||||
self.console.print(f"[cyan]Loading Qwen model: {model_name}...[/cyan]")
|
||||
|
||||
# Load model based on name
|
||||
if "qwen3" in model_name.lower():
|
||||
from transformers import Qwen3VLMoeForConditionalGeneration
|
||||
self.model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
|
||||
model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True
|
||||
)
|
||||
else:
|
||||
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
|
||||
model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True
|
||||
)
|
||||
|
||||
self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
|
||||
self.console.print(f"[green]✓ Model loaded successfully on {device}[/green]")
|
||||
|
||||
def call_qwen(
|
||||
self,
|
||||
images: list[Image.Image | str],
|
||||
prompt: str,
|
||||
) -> dict[str, str]:
|
||||
"""
|
||||
Call Qwen VLM to generate synthetic dialogue.
|
||||
|
||||
Args:
|
||||
images: List of PIL Images or image paths
|
||||
prompt: Text prompt for generation
|
||||
|
||||
Returns:
|
||||
Dictionary with keys: scenario_type, response_type, user_prompt, robot_utterance
|
||||
"""
|
||||
# Build messages with images and text
|
||||
content = []
|
||||
for img in images:
|
||||
if isinstance(img, str):
|
||||
content.append({"type": "image", "image": img})
|
||||
else:
|
||||
# PIL Image - need to save temporarily or convert
|
||||
content.append({"type": "image", "image": img})
|
||||
|
||||
content.append({"type": "text", "text": prompt})
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": content,
|
||||
}
|
||||
]
|
||||
|
||||
# Process inputs
|
||||
text = self.processor.apply_chat_template(
|
||||
messages, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
image_inputs, video_inputs = self.process_vision_info(messages)
|
||||
|
||||
inputs = self.processor(
|
||||
text=[text],
|
||||
images=image_inputs,
|
||||
videos=video_inputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
).to(self.device)
|
||||
|
||||
# Generate
|
||||
with torch.no_grad():
|
||||
generated_ids = self.model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=512,
|
||||
do_sample=True,
|
||||
temperature=self.temperature,
|
||||
)
|
||||
|
||||
# Decode response
|
||||
response = self.processor.batch_decode(
|
||||
[out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)],
|
||||
skip_special_tokens=True,
|
||||
)[0].strip()
|
||||
|
||||
return self._parse_response(response)
|
||||
|
||||
def _parse_response(self, response: str) -> dict[str, str]:
|
||||
"""Parse JSON response from model."""
|
||||
# Extract JSON from response
|
||||
if "```json" in response:
|
||||
response = response.split("```json")[1].split("```")[0]
|
||||
elif "```" in response:
|
||||
response = response.split("```")[1].split("```")[0]
|
||||
|
||||
try:
|
||||
data = json.loads(response)
|
||||
return {
|
||||
"scenario_type": data.get("scenario_type", "specific_object"),
|
||||
"response_type": data.get("response_type", "confirmation"),
|
||||
"user_prompt": data.get("user_prompt", ""),
|
||||
"robot_utterance": data.get("robot_utterance", ""),
|
||||
}
|
||||
except json.JSONDecodeError:
|
||||
# Try to find JSON object in response
|
||||
match = re.search(r"\{.*\}", response, re.DOTALL)
|
||||
if match:
|
||||
data = json.loads(match.group())
|
||||
return {
|
||||
"scenario_type": data.get("scenario_type", "specific_object"),
|
||||
"response_type": data.get("response_type", "confirmation"),
|
||||
"user_prompt": data.get("user_prompt", ""),
|
||||
"robot_utterance": data.get("robot_utterance", ""),
|
||||
}
|
||||
|
||||
raise ValueError(f"Could not parse response: {response[:200]}...")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Annotation Pipeline
|
||||
# =============================================================================
|
||||
|
||||
def load_skills_metadata(dataset_root: Path) -> dict | None:
|
||||
"""Load skills.json metadata from annotated dataset."""
|
||||
skills_path = dataset_root / "meta" / "skills.json"
|
||||
if skills_path.exists():
|
||||
with open(skills_path) as f:
|
||||
return json.load(f)
|
||||
return None
|
||||
|
||||
|
||||
def get_skill_at_timestamp(skills: list[dict], timestamp: float) -> str | None:
|
||||
"""Find which skill covers a given timestamp."""
|
||||
for skill in skills:
|
||||
if skill["start"] <= timestamp < skill["end"]:
|
||||
return skill["name"]
|
||||
# Handle last frame
|
||||
if timestamp >= skill["end"] and skill == skills[-1]:
|
||||
return skill["name"]
|
||||
return skills[-1]["name"] if skills else None
|
||||
|
||||
|
||||
def annotate_sample(
|
||||
pgen: QwenPgen,
|
||||
images: list[Image.Image | str],
|
||||
task_description: str,
|
||||
skill_history: list[str],
|
||||
skill_current: str,
|
||||
) -> dict[str, str]:
|
||||
"""
|
||||
Generate synthetic dialogue for a single sample.
|
||||
|
||||
Args:
|
||||
pgen: Qwen model wrapper
|
||||
images: List of images at current timestep
|
||||
task_description: High-level task description
|
||||
skill_history: Previous skills completed
|
||||
skill_current: Current skill being performed
|
||||
|
||||
Returns:
|
||||
Dictionary with generated dialogue
|
||||
"""
|
||||
prompt = construct_prompt(task_description, skill_history, skill_current)
|
||||
result = pgen.call_qwen(images, prompt)
|
||||
return result
|
||||
|
||||
|
||||
def generate_synthetic_data(
|
||||
dataset: LeRobotDataset,
|
||||
pgen: QwenPgen,
|
||||
skills_metadata: dict,
|
||||
image_keys: list[str],
|
||||
sample_interval_seconds: float = 1.0,
|
||||
console: Console | None = None,
|
||||
) -> tuple[pd.DataFrame, np.ndarray, list[dict]]:
|
||||
"""
|
||||
Generate synthetic dialogue data for entire dataset.
|
||||
|
||||
This function processes ALL frames in the dataset, but only calls the VLM
|
||||
at specified intervals (sample_interval_seconds). Frames between samples
|
||||
inherit the task_index from the most recent sample.
|
||||
|
||||
Args:
|
||||
dataset: LeRobot dataset with skill annotations
|
||||
pgen: Qwen model wrapper
|
||||
skills_metadata: Loaded skills.json metadata
|
||||
image_keys: List of image observation keys to use
|
||||
sample_interval_seconds: Generate dialogue every N seconds (default: 1.0)
|
||||
console: Rich console for logging
|
||||
|
||||
Returns:
|
||||
Tuple of (tasks_df, task_indices_array, debug_outputs)
|
||||
- tasks_df: DataFrame with high-level tasks (user_prompt, robot_utterance, etc.)
|
||||
- task_indices_array: Array of task indices for each frame (full dataset length)
|
||||
- debug_outputs: List of debug dictionaries (only for sampled frames)
|
||||
"""
|
||||
if console is None:
|
||||
console = Console()
|
||||
|
||||
# Extract metadata
|
||||
coarse_description = skills_metadata.get("coarse_description", "Complete the task")
|
||||
episodes = skills_metadata.get("episodes", {})
|
||||
|
||||
# Track unique high-level tasks
|
||||
high_level_tasks = {} # (user_prompt, robot_utterance, skill) -> task_index
|
||||
task_index_counter = 0 # Start at 0
|
||||
|
||||
# Array to store task index for each frame - MUST match full dataset length
|
||||
full_dataset_length = len(dataset)
|
||||
task_indices = np.zeros(full_dataset_length, dtype=np.int64)
|
||||
|
||||
# For debugging - save to JSONL
|
||||
debug_outputs = []
|
||||
|
||||
# Track sampling
|
||||
last_sample_timestamp = {} # episode_idx -> last sampled timestamp
|
||||
last_task_index = {} # episode_idx -> last generated task_index
|
||||
frames_sampled = 0
|
||||
|
||||
console.print(f"[cyan]Processing all {full_dataset_length} frames from {dataset.meta.total_episodes} episodes...[/cyan]")
|
||||
console.print(f"[cyan]Sampling interval: {sample_interval_seconds}s (fps: {dataset.meta.fps})[/cyan]")
|
||||
|
||||
# Process each frame in the FULL dataset
|
||||
for frame_idx in tqdm(range(full_dataset_length), desc="Generating synthetic dialogue"):
|
||||
try:
|
||||
# Get frame data
|
||||
frame = dataset[frame_idx]
|
||||
episode_idx = frame["episode_index"].item()
|
||||
timestamp = frame["timestamp"].item()
|
||||
|
||||
# Get episode skills
|
||||
episode_key = str(episode_idx)
|
||||
if episode_key not in episodes:
|
||||
console.print(f"[yellow]Warning: Episode {episode_idx} not in skills metadata[/yellow]")
|
||||
continue
|
||||
|
||||
episode_data = episodes[episode_key]
|
||||
skills = episode_data.get("skills", [])
|
||||
description = episode_data.get("description", coarse_description)
|
||||
|
||||
# Find current skill
|
||||
current_skill = get_skill_at_timestamp(skills, timestamp)
|
||||
if current_skill is None:
|
||||
console.print(f"[yellow]Warning: No skill found for timestamp {timestamp}[/yellow]")
|
||||
continue
|
||||
|
||||
# Determine if we should sample this frame
|
||||
should_sample = False
|
||||
|
||||
# Always sample first frame of an episode
|
||||
if episode_idx not in last_sample_timestamp:
|
||||
should_sample = True
|
||||
last_sample_timestamp[episode_idx] = timestamp
|
||||
else:
|
||||
# Sample if enough time has passed
|
||||
time_since_last = timestamp - last_sample_timestamp[episode_idx]
|
||||
if time_since_last >= sample_interval_seconds:
|
||||
should_sample = True
|
||||
last_sample_timestamp[episode_idx] = timestamp
|
||||
|
||||
# If not sampling, reuse last task index for this episode
|
||||
if not should_sample:
|
||||
if episode_idx in last_task_index:
|
||||
task_indices[frame_idx] = last_task_index[episode_idx]
|
||||
continue
|
||||
|
||||
# Sample this frame - generate synthetic dialogue
|
||||
frames_sampled += 1
|
||||
|
||||
# Build skill history (all skills before current timestamp)
|
||||
skill_history = []
|
||||
for skill in skills:
|
||||
if skill["end"] <= timestamp:
|
||||
skill_history.append(skill["name"])
|
||||
|
||||
# Load images
|
||||
images = []
|
||||
for img_key in image_keys:
|
||||
if img_key in frame:
|
||||
# Frame images are tensors (C, H, W) in [0, 1]
|
||||
img_tensor = frame[img_key]
|
||||
if len(img_tensor.shape) == 4: # (T, C, H, W)
|
||||
img_tensor = img_tensor[-1] # Take last frame
|
||||
|
||||
# Convert to PIL Image
|
||||
img_array = (img_tensor.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)
|
||||
img_pil = Image.fromarray(img_array)
|
||||
images.append(img_pil)
|
||||
|
||||
if not images:
|
||||
console.print(f"[yellow]Warning: No images found for frame {frame_idx}[/yellow]")
|
||||
continue
|
||||
|
||||
# Generate synthetic dialogue
|
||||
result = annotate_sample(
|
||||
pgen=pgen,
|
||||
images=images,
|
||||
task_description=description,
|
||||
skill_history=skill_history,
|
||||
skill_current=current_skill,
|
||||
)
|
||||
|
||||
# Create unique task key
|
||||
task_key = (
|
||||
result["user_prompt"],
|
||||
result["robot_utterance"],
|
||||
current_skill,
|
||||
result["scenario_type"],
|
||||
result["response_type"],
|
||||
)
|
||||
|
||||
# Assign or create task index
|
||||
if task_key not in high_level_tasks:
|
||||
high_level_tasks[task_key] = task_index_counter
|
||||
task_index_counter += 1
|
||||
|
||||
current_task_idx = high_level_tasks[task_key]
|
||||
task_indices[frame_idx] = current_task_idx
|
||||
last_task_index[episode_idx] = current_task_idx
|
||||
|
||||
# Save for debugging
|
||||
debug_outputs.append({
|
||||
"episode_id": int(episode_idx),
|
||||
"frame_index": frame_idx,
|
||||
"timestamp": float(timestamp),
|
||||
"skill_current": current_skill,
|
||||
"skill_history": skill_history,
|
||||
"task_description": description,
|
||||
"sampled": True,
|
||||
**result,
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
console.print(f"[red]Error processing frame {frame_idx}: {e}[/red]")
|
||||
continue
|
||||
|
||||
console.print(f"[green]✓ Sampled {frames_sampled} frames out of {full_dataset_length} total ({frames_sampled/full_dataset_length*100:.1f}%)[/green]")
|
||||
|
||||
# Create tasks DataFrame
|
||||
tasks_data = []
|
||||
for task_key, task_idx in sorted(high_level_tasks.items(), key=lambda x: x[1]):
|
||||
user_prompt, robot_utterance, skill, scenario_type, response_type = task_key
|
||||
tasks_data.append({
|
||||
"task": f"{user_prompt} | {robot_utterance}",
|
||||
"task_index": task_idx,
|
||||
"user_prompt": user_prompt,
|
||||
"robot_utterance": robot_utterance,
|
||||
"skill": skill,
|
||||
"scenario_type": scenario_type,
|
||||
"response_type": response_type,
|
||||
})
|
||||
|
||||
tasks_df = pd.DataFrame(tasks_data).set_index("task")
|
||||
|
||||
console.print(f"[green]✓ Generated {len(high_level_tasks)} unique high-level tasks[/green]")
|
||||
|
||||
return tasks_df, task_indices, debug_outputs
|
||||
|
||||
|
||||
def save_high_level_tasks(
|
||||
tasks_df: pd.DataFrame,
|
||||
dataset_root: Path,
|
||||
console: Console | None = None,
|
||||
) -> None:
|
||||
"""Save high-level tasks to tasks_high_level.parquet."""
|
||||
if console is None:
|
||||
console = Console()
|
||||
|
||||
output_path = dataset_root / "meta" / "tasks_high_level.parquet"
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
tasks_df.to_parquet(output_path, engine="pyarrow", compression="snappy")
|
||||
console.print(f"[green]✓ Saved high-level tasks to {output_path}[/green]")
|
||||
|
||||
|
||||
def save_debug_outputs(
|
||||
debug_outputs: list[dict],
|
||||
dataset_root: Path,
|
||||
console: Console | None = None,
|
||||
) -> None:
|
||||
"""Save debug outputs to JSONL file."""
|
||||
if console is None:
|
||||
console = Console()
|
||||
|
||||
output_path = dataset_root / "meta" / "syn_annotations.jsonl"
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with open(output_path, "w") as f:
|
||||
for item in debug_outputs:
|
||||
f.write(json.dumps(item) + "\n")
|
||||
|
||||
console.print(f"[green]✓ Saved debug annotations to {output_path}[/green]")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Main Entry Point
|
||||
# =============================================================================
|
||||
|
||||
def main():
|
||||
"""Main entry point for synthetic data generation."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Generate synthetic dialogue data for hierarchical robot policies",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog=textwrap.dedent("""\
|
||||
Examples:
|
||||
# Generate synthetic data for a dataset
|
||||
python annotate_pgen.py --repo-id lerobot/svla_so101_pickplace \\
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \\
|
||||
--output-dir ./output
|
||||
|
||||
# Use Qwen3 model with custom parameters
|
||||
python annotate_pgen.py --repo-id lerobot/svla_so101_pickplace \\
|
||||
--model Qwen/Qwen3-VL-30B-A3B-Instruct \\
|
||||
--temperature 0.8 \\
|
||||
--batch-size 1
|
||||
"""),
|
||||
)
|
||||
|
||||
# Data source
|
||||
data_group = parser.add_mutually_exclusive_group(required=True)
|
||||
data_group.add_argument("--data-dir", type=str, help="Path to local LeRobot dataset")
|
||||
data_group.add_argument("--repo-id", type=str, help="HuggingFace Hub dataset repository ID")
|
||||
|
||||
# Model configuration
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
type=str,
|
||||
default="Qwen/Qwen2-VL-7B-Instruct",
|
||||
help="VLM model to use (default: Qwen/Qwen2-VL-7B-Instruct)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cuda",
|
||||
help="Device to run model on (default: cuda)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dtype",
|
||||
type=str,
|
||||
default="bfloat16",
|
||||
choices=["bfloat16", "float16", "float32"],
|
||||
help="Model dtype (default: bfloat16)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--temperature",
|
||||
type=float,
|
||||
default=0.7,
|
||||
help="Sampling temperature (default: 0.7)",
|
||||
)
|
||||
|
||||
# Processing options
|
||||
parser.add_argument(
|
||||
"--batch-size",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Batch size for processing (default: 1) [currently unused]",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num-image-views-per-sample",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of camera views to use per sample (default: 1)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--sample-interval",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="Generate dialogue every N seconds (default: 1.0). Frames between samples reuse the last generated dialogue. "
|
||||
"Use larger intervals (e.g., 2.0 or 5.0) for faster processing during testing.",
|
||||
)
|
||||
|
||||
# Output options
|
||||
parser.add_argument(
|
||||
"--output-dir",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Output directory for modified dataset",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--push-to-hub",
|
||||
action="store_true",
|
||||
help="Push modified dataset to HuggingFace Hub",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
console = Console()
|
||||
|
||||
# Load dataset
|
||||
console.print("[cyan]Loading dataset...[/cyan]")
|
||||
if args.data_dir:
|
||||
dataset = LeRobotDataset(repo_id="local/dataset", root=args.data_dir)
|
||||
dataset_root = Path(args.data_dir)
|
||||
else:
|
||||
dataset = LeRobotDataset(repo_id=args.repo_id)
|
||||
dataset_root = dataset.root
|
||||
|
||||
console.print(f"[green]✓ Loaded dataset with {len(dataset)} frames[/green]")
|
||||
|
||||
# Load skills metadata
|
||||
console.print("[cyan]Loading skills metadata...[/cyan]")
|
||||
skills_metadata = load_skills_metadata(dataset_root)
|
||||
if skills_metadata is None:
|
||||
console.print("[red]Error: No skills.json found. Run annotate.py first![/red]")
|
||||
return
|
||||
|
||||
console.print(f"[green]✓ Loaded skills for {len(skills_metadata.get('episodes', {}))} episodes[/green]")
|
||||
|
||||
# Initialize model
|
||||
dtype_map = {
|
||||
"bfloat16": torch.bfloat16,
|
||||
"float16": torch.float16,
|
||||
"float32": torch.float32,
|
||||
}
|
||||
torch_dtype = dtype_map[args.dtype]
|
||||
|
||||
console.print(f"[cyan]Initializing {args.model}...[/cyan]")
|
||||
pgen = QwenPgen(
|
||||
model_name=args.model,
|
||||
device=args.device,
|
||||
torch_dtype=torch_dtype,
|
||||
temperature=args.temperature,
|
||||
)
|
||||
|
||||
# Get image keys
|
||||
image_keys = dataset.meta.camera_keys[:args.num_image_views_per_sample]
|
||||
console.print(f"[cyan]Using image keys: {image_keys}[/cyan]")
|
||||
|
||||
# Generate synthetic data
|
||||
tasks_df, task_indices, debug_outputs = generate_synthetic_data(
|
||||
dataset=dataset,
|
||||
pgen=pgen,
|
||||
skills_metadata=skills_metadata,
|
||||
image_keys=image_keys,
|
||||
sample_interval_seconds=args.sample_interval,
|
||||
console=console,
|
||||
)
|
||||
|
||||
# Save high-level tasks
|
||||
save_high_level_tasks(tasks_df, dataset_root, console)
|
||||
save_debug_outputs(debug_outputs, dataset_root, console)
|
||||
|
||||
# Add task_index_high_level feature to dataset
|
||||
console.print("[cyan]Adding task_index_high_level feature to dataset...[/cyan]")
|
||||
|
||||
# Determine output directory
|
||||
if args.output_dir:
|
||||
output_dir = Path(args.output_dir)
|
||||
repo_id = f"{dataset.repo_id}_with_high_level_tasks"
|
||||
else:
|
||||
output_dir = None
|
||||
repo_id = f"{dataset.repo_id}_with_high_level_tasks"
|
||||
|
||||
# Add feature using dataset_tools
|
||||
feature_info = {
|
||||
"dtype": "int64",
|
||||
"shape": (1,),
|
||||
"names": None,
|
||||
}
|
||||
breakpoint()
|
||||
new_dataset = add_features(
|
||||
dataset=dataset,
|
||||
features={
|
||||
"task_index_high_level": (task_indices, feature_info),
|
||||
},
|
||||
output_dir=output_dir,
|
||||
repo_id=repo_id,
|
||||
)
|
||||
|
||||
console.print(f"[bold green]✓ Successfully added task_index_high_level feature![/bold green]")
|
||||
console.print(f" New dataset saved to: {new_dataset.root}")
|
||||
console.print(f" Total high-level tasks: {len(tasks_df)}")
|
||||
|
||||
# Push to hub if requested
|
||||
if args.push_to_hub:
|
||||
if args.data_dir:
|
||||
console.print("[yellow]Warning: --push-to-hub requires --repo-id, skipping...[/yellow]")
|
||||
else:
|
||||
console.print("[cyan]Pushing to HuggingFace Hub...[/cyan]")
|
||||
try:
|
||||
new_dataset.push_to_hub(push_videos=False)
|
||||
console.print(f"[green]✓ Pushed to {repo_id}[/green]")
|
||||
except Exception as e:
|
||||
console.print(f"[red]Push failed: {e}[/red]")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@@ -0,0 +1,143 @@
|
||||
# Example: Synthetic Data Generation with Sampling
|
||||
|
||||
## Quick Start
|
||||
|
||||
### 1. Test with 100 frames and 1 second sampling
|
||||
```bash
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \
|
||||
--num-samples 100 \
|
||||
--sample-interval 1.0 \
|
||||
--output-dir ./outputs/test_pgen
|
||||
```
|
||||
|
||||
**Expected behavior** (assuming 30 fps):
|
||||
- Total frames: 100
|
||||
- Frames sampled: ~4 (every 30 frames = 1 second)
|
||||
- Efficiency: 96% fewer VLM calls
|
||||
- Output: All 100 frames get `task_index_high_level`, but only 4 unique dialogues generated
|
||||
|
||||
### 2. Process full dataset with different sampling rates
|
||||
|
||||
#### Conservative (every 2 seconds)
|
||||
```bash
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \
|
||||
--sample-interval 2.0 \
|
||||
--output-dir ./outputs/pgen_2s
|
||||
```
|
||||
|
||||
#### Standard (every 1 second) - **RECOMMENDED**
|
||||
```bash
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \
|
||||
--sample-interval 1.0 \
|
||||
--output-dir ./outputs/pgen_1s
|
||||
```
|
||||
|
||||
#### Fine-grained (every 0.5 seconds)
|
||||
```bash
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \
|
||||
--sample-interval 0.5 \
|
||||
--output-dir ./outputs/pgen_0.5s
|
||||
```
|
||||
|
||||
## Performance Estimates
|
||||
|
||||
For a dataset with:
|
||||
- 100 episodes
|
||||
- 10 seconds per episode (average)
|
||||
- 30 fps
|
||||
- Total frames: 30,000
|
||||
|
||||
| Sampling Interval | Frames Sampled | % Sampled | Speedup | Time Estimate |
|
||||
|-------------------|----------------|-----------|---------|---------------|
|
||||
| Every frame (0.033s) | 30,000 | 100% | 1x | ~10 hours |
|
||||
| 0.5 seconds | 2,000 | 6.7% | 15x | ~40 min |
|
||||
| **1.0 seconds** | **1,000** | **3.3%** | **30x** | **~20 min** |
|
||||
| 2.0 seconds | 500 | 1.7% | 60x | ~10 min |
|
||||
|
||||
*Note: Times are approximate and depend on GPU, model size, and generation speed*
|
||||
|
||||
## Understanding the Output
|
||||
|
||||
### Console Output Example
|
||||
```
|
||||
[cyan]Generating synthetic data for 30000 frames...[/cyan]
|
||||
[cyan]Sampling interval: 1.0s (fps: 30)[/cyan]
|
||||
Generating synthetic dialogue: 100%|████████| 30000/30000 [20:15<00:00, 24.68it/s]
|
||||
[green]✓ Sampled 1000 frames out of 30000 (3.3%)[/green]
|
||||
[green]✓ Generated 450 unique high-level tasks[/green]
|
||||
```
|
||||
|
||||
### What happens:
|
||||
1. **Frame 0 (t=0.0s)**: Generate dialogue → Task index 0
|
||||
2. **Frames 1-29 (t=0.033s-0.967s)**: Reuse task index 0
|
||||
3. **Frame 30 (t=1.0s)**: Generate new dialogue → Task index 1
|
||||
4. **Frames 31-59 (t=1.033s-1.967s)**: Reuse task index 1
|
||||
5. And so on...
|
||||
|
||||
### Result:
|
||||
- Every frame has a `task_index_high_level`
|
||||
- Only sampled frames have unique dialogues generated
|
||||
- Intermediate frames inherit from the most recent sample
|
||||
- Maintains temporal coherence within episodes
|
||||
|
||||
## Checking Your Results
|
||||
|
||||
After running, verify the output:
|
||||
|
||||
```bash
|
||||
# Check the generated tasks
|
||||
python -c "
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
|
||||
tasks = pd.read_parquet('outputs/test_pgen/meta/tasks_high_level.parquet')
|
||||
print(f'Total unique tasks: {len(tasks)}')
|
||||
print(f'Sample tasks:')
|
||||
print(tasks[['user_prompt', 'robot_utterance', 'skill']].head())
|
||||
"
|
||||
|
||||
# Check debug output
|
||||
head outputs/test_pgen/meta/syn_annotations.jsonl
|
||||
|
||||
# Load and verify dataset
|
||||
python -c "
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
|
||||
ds = LeRobotDataset(repo_id='local_with_high_level_tasks',
|
||||
root='outputs/test_pgen')
|
||||
print(f'Dataset has {len(ds)} frames')
|
||||
print(f'Features: {list(ds.features.keys())}')
|
||||
assert 'task_index_high_level' in ds.features
|
||||
print('✓ task_index_high_level feature added successfully!')
|
||||
"
|
||||
```
|
||||
|
||||
## Common Use Cases
|
||||
|
||||
### Development/Testing
|
||||
```bash
|
||||
--sample-interval 2.0 # Fast iteration
|
||||
--num-samples 500 # Small subset
|
||||
```
|
||||
|
||||
### Production Training
|
||||
```bash
|
||||
--sample-interval 1.0 # Good coverage
|
||||
# Process all samples (no --num-samples)
|
||||
```
|
||||
|
||||
### High-Quality Dataset
|
||||
```bash
|
||||
--sample-interval 0.5 # Fine-grained
|
||||
--temperature 0.6 # More consistent
|
||||
--model Qwen/Qwen3-VL-30B-A3B-Instruct # Larger model
|
||||
```
|
||||
|
||||
@@ -0,0 +1,334 @@
|
||||
Generate annotate_pgen.py using Qwen for synthetic data generation
|
||||
|
||||
You are writing a Python script called annotate_pgen.py.
|
||||
This script generates synthetic user prompts (ℓ_t) and robot utterances (u_t) for Hi Robot–style hierarchical policy training, using Qwen 3vl as the generator model (pgen).
|
||||
|
||||
SCRIPT PURPOSE
|
||||
|
||||
The script must:
|
||||
|
||||
Load Dlabeled which is a LeRobot Dataset that has been annotate using the annotate.py script, which contains:
|
||||
|
||||
images: list of image paths at time t
|
||||
|
||||
skill_current: the annotated skill label (ℓ̂_t)
|
||||
|
||||
skill_history: list of previous skill labels (ℓ̂₀ … ℓ̂_{t−1}), those where annotated, and you can find details on them stored in teh dataset inside the the DATA_PATH/meta/skills.json
|
||||
|
||||
you will find something like
|
||||
|
||||
{
|
||||
"coarse_description": "pink lego brick into the transparent box",
|
||||
"skill_to_task_index": {
|
||||
"robot arm picks up pink lego brick": 19,
|
||||
"robot arm approaches transparent box": 3,
|
||||
"robot arm retracts from transparent box": 28,
|
||||
"robot arm moves towards pink lego brick": 12,
|
||||
"robot arm releases red lego brick into box": 26,
|
||||
"robot arm releases red lego brick into transparent box": 27,
|
||||
"robot arm closes gripper to pick up the pink lego brick": 5,
|
||||
"robot arm lifts the pink lego brick": 7,
|
||||
etc..
|
||||
},
|
||||
"episodes": {
|
||||
"0": {
|
||||
"episode_index": 0,
|
||||
"description": "pink lego brick into the transparent box",
|
||||
"skills": [
|
||||
{
|
||||
"name": "robot arm moves towards pink lego brick",
|
||||
"start": 0.0,
|
||||
"end": 1.8
|
||||
},
|
||||
{
|
||||
"name": "robot arm picks up pink lego brick",
|
||||
"start": 1.8,
|
||||
"end": 3.1
|
||||
},
|
||||
{
|
||||
"name": "robot arm moves towards transparent box",
|
||||
"start": 3.1,
|
||||
"end": 5.5
|
||||
},
|
||||
{
|
||||
"name": "robot arm releases pink lego brick into transparent box",
|
||||
"start": 5.5,
|
||||
"end": 7.0
|
||||
},
|
||||
{
|
||||
"name": "robot arm retracts from transparent box",
|
||||
"start": 7.0,
|
||||
"end": 10.1
|
||||
}
|
||||
]
|
||||
},
|
||||
"1": {
|
||||
"episode_index": 1,
|
||||
"description": "pink lego brick into the transparent box",
|
||||
"skills": [
|
||||
{
|
||||
"name": "robot arm moves towards red lego brick",
|
||||
"start": 0.0,
|
||||
"end": 1.2
|
||||
},
|
||||
{
|
||||
"name": "robot arm picks up red lego brick",
|
||||
"start": 1.2,
|
||||
"end": 2.0
|
||||
},
|
||||
{
|
||||
"name": "robot arm moves towards transparent box",
|
||||
"start": 2.0,
|
||||
"end": 3.8
|
||||
},
|
||||
{
|
||||
"name": "robot arm places red lego brick into transparent box",
|
||||
"start": 3.8,
|
||||
"end": 5.0
|
||||
},
|
||||
{
|
||||
"name": "robot arm moves away from transparent box",
|
||||
"start": 5.0,
|
||||
"end": 8.9
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
notice how task_description: is a high-level description (e.g., "make a sandwich") stored in description for each episode
|
||||
|
||||
For each sample, call Qwen VLM to generate:
|
||||
|
||||
synthetic user prompt ℓ_t
|
||||
|
||||
synthetic robot response u_t
|
||||
|
||||
Save results to D_syn in Parquet format insdie DATA_PATH/meta/tasks.parquet ; note tasks.parquet already contains the other tasks, so you need to update
|
||||
|
||||
Should be modular, clean, easy to extend, with:
|
||||
|
||||
a PGEN_PROMPT_TEMPLATE
|
||||
|
||||
a construct_prompt() method
|
||||
|
||||
a call_qwen() method
|
||||
|
||||
a annotate_sample() method
|
||||
|
||||
a CLI entrypoint (if __name__ == "__main__":)
|
||||
|
||||
📦 INPUT FORMAT (Dlabeled)
|
||||
|
||||
The script should expect Dlabeled as a .jsonl file where each line has:
|
||||
|
||||
{
|
||||
"episode_id": "ep_001",
|
||||
"t": 37,
|
||||
"images": ["path/to/cam0_t.jpg", "path/to/cam1_t.jpg"],
|
||||
"skill_current": "pick up the KitKat",
|
||||
"skill_history": ["open fridge", "pick up lettuce", "place lettuce"],
|
||||
"task_description": "making a sandwich"
|
||||
}
|
||||
|
||||
📤 OUTPUT FORMAT (D_syn)
|
||||
|
||||
Each line of synthetically generated data should be:
|
||||
|
||||
{
|
||||
"episode_id": "ep_001",
|
||||
"t": 37,
|
||||
"images": ["path/to/cam0_t.jpg", "path/to/cam1_t.jpg"],
|
||||
"skill_current": "pick up the KitKat",
|
||||
"skill_history": [...],
|
||||
"user_prompt": "Can you grab me something sweet?",
|
||||
"robot_utterance": "Sure, I can pick up the KitKat.",
|
||||
"task_description": "making a sandwich"
|
||||
}
|
||||
|
||||
|
||||
Store as syn_annotations.jsonl. for debugging
|
||||
|
||||
🧠 pgen MODEL (Qwen) REQUIREMENTS
|
||||
|
||||
Use HuggingFace Transformers:
|
||||
|
||||
Qwen/Qwen2-VL-7B-Instruct (or any Qwen2-VL Vision-Language model available)
|
||||
|
||||
Use the image + text chat interface
|
||||
|
||||
Vision inputs should be loaded with PIL
|
||||
|
||||
Use a single forward pass that outputs BOTH ℓ_t and u_t in a structured JSON
|
||||
|
||||
📝 PROMPT FORMAT FOR pgen
|
||||
|
||||
Create a template like:
|
||||
|
||||
You are a robot-assistant dialogue generator for hierarchical robot policies.
|
||||
|
||||
You will receive:
|
||||
- A list of images showing the current robot scene.
|
||||
- The high-level task: {task_description}
|
||||
- Previous skill steps completed: {skill_history}
|
||||
- The next skill to be performed by the robot: {skill_current}
|
||||
|
||||
Generate two things in JSON:
|
||||
1. "user_prompt": a natural-sounding user request that logically leads to the robot performing the skill "{skill_current}" given the task and history.
|
||||
2. "robot_utterance": a natural robot reply acknowledging or clarifying the request.
|
||||
|
||||
The responses must be grounded in the visual scene, the task, and the skill history.
|
||||
|
||||
Respond ONLY in JSON:
|
||||
{
|
||||
"user_prompt": "...",
|
||||
"robot_utterance": "..."
|
||||
}
|
||||
|
||||
This resposne will have a corresponsing task_index, and the task will be saved in task.parqeut and you must update each dataset parquet in for example /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace/data/chunk-000/
|
||||
file-000.parquet to include this new feature called task_index_high_level consider udpatign the metadata in info.json as well
|
||||
📌 LOGIC REQUIRED
|
||||
construct_prompt(sample)
|
||||
|
||||
Loads sample dict
|
||||
|
||||
Inserts:
|
||||
|
||||
task_description
|
||||
|
||||
skill_history
|
||||
|
||||
skill_current
|
||||
|
||||
Returns a full text prompt string
|
||||
|
||||
call_qwen(images, prompt)
|
||||
|
||||
Loads images into Qwen-VL multimodal input format
|
||||
|
||||
Calls model.generate
|
||||
|
||||
Parses JSON output
|
||||
|
||||
annotate_sample(sample)
|
||||
|
||||
Builds prompt
|
||||
|
||||
Calls Qwen
|
||||
|
||||
Returns augmented sample with user_prompt + robot_utterance
|
||||
|
||||
🚀 CLI Usage
|
||||
|
||||
The script should run as:
|
||||
|
||||
python annotate_pgen.py \
|
||||
--output-dir PATH \
|
||||
--model Qwen/Qwen2-VL-7B-Instruct \
|
||||
--repo-id lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen3-VL-30B-A3B-Instruct \
|
||||
--batch-size 1
|
||||
|
||||
|
||||
Include arguments via argparse.
|
||||
|
||||
🔧 OTHER REQUIREMENTS
|
||||
|
||||
Use tqdm for progress bars
|
||||
|
||||
Log errors gracefully and continue
|
||||
|
||||
Support GPU acceleration (device="cuda")
|
||||
|
||||
Cache model loading so it's not reloaded every call
|
||||
|
||||
Make the prompt deterministic but allow temperature parameter
|
||||
|
||||
Add a flag --num-image-views-per-sample
|
||||
|
||||
Add automatic JSON parsing with helpful error messages
|
||||
|
||||
🎯 FINAL DELIVERABLE
|
||||
|
||||
Cursor must now generate:
|
||||
A full Python file named annotate_pgen.py implementing the above functionality end-to-end.
|
||||
|
||||
It should be production-ready, runnable on real data, cleanly structured, and easy to modify.
|
||||
|
||||
|
||||
from the paper:
|
||||
Next, we use a large vision-language model (VLM) pgen
|
||||
to produce synthetic user prompts and interjections ℓt,
|
||||
and corresponding robot utterance ut. Given Dlabeled, we
|
||||
prompt pgen with both the visual context I1
|
||||
t ,...,In
|
||||
t and the
|
||||
skill labelˆ
|
||||
ℓt (e.g., pick up the lettuce). pgen then imag-
|
||||
ines an appropriate interaction that might have led toˆ
|
||||
ℓt in a
|
||||
real user interaction: it generates possible user prompts ℓt
|
||||
(e.g., “Can you add some lettuce for me?”) along with the
|
||||
robot’s verbal responses and clarifications ut. We detail the
|
||||
A. Synthetic Data Generation
|
||||
A.1. Scenario and Response Categorization
|
||||
To ensure the quality and diversity of the synthetic data,
|
||||
we incorporate structured scenario classification and re-
|
||||
sponse categorization into the prompt design for pgen, fol-
|
||||
lowing (Stephan et al., 2024). Specifically, we classify
|
||||
interactions into different scenario types, such as nega-
|
||||
tive task (where the user instructs the robot what not to
|
||||
do), situated correction (where the user adjusts an earlier
|
||||
command based on the evolving task state), and specific
|
||||
constraint (where the user specifies particular constraints,
|
||||
such as dietary preferences). In addition, we categorize
|
||||
the robot’s responses into types such as simple confirma-
|
||||
tions, clarifications, and error handling. These classifica-
|
||||
tions guide the generation process to ensure a broad range
|
||||
of user-robot interactions.
|
||||
A.2. Prompt Construction for Contextual Grounding
|
||||
In prompt P, we include a detailed description of the task
|
||||
(e.g., bussing a table, making a sandwich, grocery shop-
|
||||
ping) and instruct the model to ground responses in visual
|
||||
observations and prior context. A key advantage of lever-
|
||||
aging large pretrained VLMs is their ability to incorporate
|
||||
world knowledge when generating interactions. For in-
|
||||
stance, the model can infer dietary constraints when gener-
|
||||
ating prompts for sandwich-making, producing user com-
|
||||
mands such as “Can you make a sandwich for me? I’m
|
||||
lactose intolerant” and an appropriate robot response like
|
||||
“Sure, I won’t put cheese on it.” Similarly, it can reason
|
||||
over ambiguous or implicit requests, such as inferring that
|
||||
“I want something sweet” in a grocery shopping scenario
|
||||
should lead to suggestions like chocolate or candy.
|
||||
To maintain consistency in multi-step tasks, we condition
|
||||
pgen on prior skill labels within an episodeˆ
|
||||
ˆ
|
||||
ℓ0,...,
|
||||
ℓt−1,
|
||||
allowing it to generate coherent user commands that
|
||||
account for past actions. For instance, if the robot
|
||||
has already placed lettuce and tomato on a sandwich,
|
||||
the generated user prompt might request additional in-
|
||||
gredients that logically follow. This ensures that the
|
||||
synthetic interactions reflect realistic task progression
|
||||
rather than isolated commands. As such, we leverage
|
||||
ˆ
|
||||
ˆ
|
||||
ˆ
|
||||
pgen(ℓt,ut|I1
|
||||
t ,...,In
|
||||
t ,
|
||||
ℓ0,...,
|
||||
ℓt−1,
|
||||
ℓt,P) to produce a richer,
|
||||
more diverse synthetic dataset Dsyn that provides mean-
|
||||
ingful supervision for training our high-level policy.
|
||||
While in this work we generate a separate Dsyn and train
|
||||
a separate high-level policy for each task (e.g., sandwich
|
||||
making vs. table cleaning) for clarity and ease of bench-
|
||||
marking, the architecture is readily amenable to a unified
|
||||
multi-task formulation. In principle, the same hierarchical
|
||||
approach could be used to train a single high-level policy
|
||||
across a multitude of tasks, facilitating knowledge transfer
|
||||
|
||||
|
||||
The result should be a new LeRobotDataset with a new feature called task_index_high_level inside each dataset parquet
|
||||
Executable
+31
@@ -0,0 +1,31 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Example script to run synthetic data generation with Qwen VLM
|
||||
# This generates user prompts and robot utterances for hierarchical policy training
|
||||
|
||||
# Configuration
|
||||
REPO_ID="lerobot/svla_so101_pickplace"
|
||||
MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
|
||||
# Alternative: MODEL="Qwen/Qwen2-VL-7B-Instruct"
|
||||
|
||||
|
||||
OUTPUT_DIR="/fsx/jade_choghari/outputs/pgen_annotations"
|
||||
BATCH_SIZE=1
|
||||
TEMPERATURE=0.7
|
||||
SAMPLE_INTERVAL=1.0 # Generate dialogue every 1 second (all episodes processed)
|
||||
|
||||
# Run synthetic data generation (processes ALL episodes)
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--repo-id "$REPO_ID" \
|
||||
--model "$MODEL" \
|
||||
--output-dir "$OUTPUT_DIR" \
|
||||
--temperature "$TEMPERATURE" \
|
||||
--sample-interval "$SAMPLE_INTERVAL" \
|
||||
--num-image-views-per-sample 1
|
||||
|
||||
# For faster testing, increase sample interval:
|
||||
# --sample-interval 5.0 # Samples every 5 seconds (much faster)
|
||||
|
||||
# To push to hub after generation:
|
||||
# Add --push-to-hub flag
|
||||
|
||||
Executable
+44
@@ -0,0 +1,44 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Quick test to verify the fix for task_indices length mismatch
|
||||
# This should now work correctly even with --num-samples < full dataset length
|
||||
|
||||
echo "Testing annotate_pgen.py with --num-samples=100 on full dataset..."
|
||||
|
||||
python examples/dataset/annotate_pgen.py \
|
||||
--data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
|
||||
--model Qwen/Qwen3-VL-30B-A3B-Instruct \
|
||||
--num-samples 100 \
|
||||
--sample-interval 1.0 \
|
||||
--output-dir /fsx/jade_choghari/outputs/pgen_test_fixed
|
||||
|
||||
if [ $? -eq 0 ]; then
|
||||
echo "✓ SUCCESS: Script completed without errors!"
|
||||
echo ""
|
||||
echo "Verifying output..."
|
||||
|
||||
# Check that all frames have task_index_high_level
|
||||
python -c "
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
import numpy as np
|
||||
|
||||
ds = LeRobotDataset(repo_id='local_test', root='/fsx/jade_choghari/outputs/pgen_test_fixed')
|
||||
print(f'Dataset has {len(ds)} frames')
|
||||
print(f'Features: {list(ds.features.keys())}')
|
||||
|
||||
# Check that task_index_high_level exists
|
||||
assert 'task_index_high_level' in ds.features, 'task_index_high_level not in features!'
|
||||
|
||||
# Sample some frames
|
||||
for idx in [0, 50, 99, 100, 500, 1000, 11938]:
|
||||
if idx < len(ds):
|
||||
frame = ds[idx]
|
||||
task_idx = frame['task_index_high_level'].item()
|
||||
print(f'Frame {idx}: task_index_high_level = {task_idx}')
|
||||
|
||||
print('✓ All checks passed!')
|
||||
"
|
||||
else
|
||||
echo "✗ FAILED: Script exited with error code $?"
|
||||
fi
|
||||
|
||||
Reference in New Issue
Block a user