| --- |
| 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` |
|
|
