Datasets:
File size: 4,861 Bytes
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license: apache-2.0
language:
- en
- zh
pretty_name: Robot-Use-Agent-SFTData
size_categories:
- 10K<n<100K
task_categories:
- image-text-to-text
- robotics
tags:
- robotics
- embodied-ai
- agent
- sft
- code-as-policy
- multi-turn
- llama-factory
- axolotl
- qwen2-vl
---
# Robot-Use-Agent-SFTData
A comprehensive multi-turn multimodal dataset for supervised fine-tuning (SFT) of vision-language embodied robot agents, converted from the standardized `robot-control-trace/v1` format.
Compatible out-of-the-box with **Hugging Face Datasets**, **LLaMA-Factory**, **Axolotl**, and **CaPtain-SFT**.
## Dataset Statistics
| Metric | Value |
|---|---|
| **Total Episodes** | 759 episodes |
| **Total Interaction Steps** | 34,343 steps |
| **Camera Frames** | 113,609 images (5.6 GB) |
| **Cameras** | Multi-view (`cam_head`, `cam_left_wrist`, `cam_right_wrist`) |
| **Format** | Multi-turn conversational `messages` with tool calls & CoT reasoning |
| **Benchmarks & Sources** | RoboDojo, RoboTwin (92 manipulation tasks), Local Codex ARX X5 real rollouts, Remote audit clusters |
## Files & Structure
```
Robot-Use-Agent-SFTData/
├── sft.jsonl # Core SFT training rows (ShareGPT / CaPtain multimodal messages)
├── dataset_info.json # LLaMA-Factory direct registration config
├── manifest.json # Master metadata catalog of all 759 episodes
├── images_manifest.tsv # TSV index of all 113,609 image files
├── images.tar # Complete image archive (5.6 GB)
├── unpack_images.py # One-click image unpacker
└── README.md # Dataset card & documentation
```
## Quick Start & Usage
### 1. Download & Unpack Images
```bash
# Clone repository
git clone https://huggingface.co/datasets/Candlest/Robot-Use-Agent-SFTData
cd Robot-Use-Agent-SFTData
# Unpack camera observations (takes ~15 seconds)
python3 unpack_images.py
```
### 2. Load with Hugging Face Datasets
```python
from datasets import load_dataset
dataset = load_dataset("json", data_files="sft.jsonl")
print("Total training episodes:", len(dataset["train"]))
first_sample = dataset["train"][0]
print("Episode ID:", first_sample["id"])
print("Total Turns:", len(first_sample["messages"]))
print("Chain of Thought:", first_sample["messages"][2]["reasoning_content"])
```
### 3. Fine-tuning with LLaMA-Factory (Qwen2.5-VL / Qwen3.5-VL)
1. Register the dataset in LLaMA-Factory by merging or symlinking `dataset_info.json`:
```json
{
"captain_robot_sft": {
"file_name": "sft.jsonl",
"formatting": "sharegpt",
"columns": {
"messages": "messages",
"images": "images",
"tools": "tools"
}
}
}
```
2. Launch training:
```bash
llamafactory-cli train \
--stage sft \
--do_train \
--model_name_or_path Qwen/Qwen2.5-VL-7B-Instruct \
--dataset captain_robot_sft \
--dataset_dir /path/to/Robot-Use-Agent-SFTData \
--template qwen2_vl \
--finetuning_type lora \
--output_dir ./output/robot_vlm_lora \
--cutoff_len 4096 \
--learning_rate 1e-4 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8
```
## Row Schema
Each line in `sft.jsonl` follows standard multi-turn tool-use format:
```json
{
"id": "robodojo_fold_clothes_L1#full",
"images": [
"images/robodojo_fold_clothes_L1_0000_cam_head.jpg",
"images/robodojo_fold_clothes_L1_0000_cam_left_wrist.jpg",
"images/robodojo_fold_clothes_L1_0000_cam_right_wrist.jpg"
],
"tools": [{
"type": "function",
"function": {
"name": "robot_controller",
"description": "Execute robot primitive actions, motion commands, or skill policies on the robot.",
"parameters": { ... }
}
}],
"messages": [
{"role": "system", "content": "You are an embodied robotic agent..."},
{"role": "user", "content": [
{"type": "text", "text": "Task: Fold the clothes neatly.\nInitial camera observations:"},
{"type": "image", "image": "images/robodojo_fold_clothes_L1_0000_cam_head.jpg"},
{"type": "image", "image": "images/robodojo_fold_clothes_L1_0000_cam_left_wrist.jpg"},
{"type": "image", "image": "images/robodojo_fold_clothes_L1_0000_cam_right_wrist.jpg"}
]},
{"role": "assistant", "content": "Executing: fold_cloth_left", "reasoning_content": "Plan: Approach the corner of the cloth...", "tool_calls": [...]},
{"role": "tool", "tool_call_id": "call_robodojo_fold_clothes_L1_step_0000", "content": [{"type": "text", "text": "ok"}]}
],
"meta": {
"episode": "robodojo_fold_clothes_L1",
"benchmark": "robodojo",
"task": "fold_clothes",
"total_steps": 15,
"n_images": 45,
"supervise_turns": [2, 5, 8, ...],
"host_final_success": true
}
}
```
## Citation & License
- License: Apache-2.0
- Underlying Traces Schema: `robot-control-trace/v1`
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