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Datasets:
Candlest
/
Robot-Use-Agent-SFTData

Tasks:
Image-Text-to-Text
Robotics
Languages:
English
Chinese
Size:
10K<n<100K
Tags:
robotics
embodied-ai
agent
sft
code-as-policy
multi-turn
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
images: list<item: string>
  child 0, item: string
tools: list<item: struct<type: string, function: struct<name: string, description: string, parameters: stru (... 287 chars omitted)
  child 0, item: struct<type: string, function: struct<name: string, description: string, parameters: struct<type: st (... 275 chars omitted)
      child 0, type: string
      child 1, function: struct<name: string, description: string, parameters: struct<type: string, properties: struct<comman (... 243 chars omitted)
          child 0, name: string
          child 1, description: string
          child 2, parameters: struct<type: string, properties: struct<commands: struct<type: string, items: struct<type: string>,  (... 188 chars omitted)
              child 0, type: string
              child 1, properties: struct<commands: struct<type: string, items: struct<type: string>, description: string>, skill: stru (... 124 chars omitted)
                  child 0, commands: struct<type: string, items: struct<type: string>, description: string>
                      child 0, type: string
                      child 1, items: struct<type: string>
                          child 0, type: string
                      child 2, description: string
                  child 1, skill: struct<type: string, description: string>
                      child 0, type: string
                      child 1, description: string
                  child 2, target_point: struct<type: string, items: struct<type: 
...
tion: struct<name: string, arguments: string>>>
          child 0, item: struct<id: string, type: string, function: struct<name: string, arguments: string>>
              child 0, id: string
              child 1, type: string
              child 2, function: struct<name: string, arguments: string>
                  child 0, name: string
                  child 1, arguments: string
      child 4, tool_call_id: string
meta: struct<episode: string, source: string, benchmark: string, task: string, instruction: string, total_ (... 92 chars omitted)
  child 0, episode: string
  child 1, source: string
  child 2, benchmark: string
  child 3, task: string
  child 4, instruction: string
  child 5, total_steps: int64
  child 6, n_images: int64
  child 7, supervise_turns: list<item: int64>
      child 0, item: int64
  child 8, host_final_success: bool
total_episodes: int64
created_at: timestamp[s]
benchmarks: list<item: string>
  child 0, item: string
schema: string
episodes: list<item: struct<id: string, steps: int64, images: int64, benchmark: string, task: string, prompt:  (... 23 chars omitted)
  child 0, item: struct<id: string, steps: int64, images: int64, benchmark: string, task: string, prompt: string, mod (... 11 chars omitted)
      child 0, id: string
      child 1, steps: int64
      child 2, images: int64
      child 3, benchmark: string
      child 4, task: string
      child 5, prompt: string
      child 6, model: string
total_steps: int64
name: string
total_images: int64
to
{'schema': Value('string'), 'name': Value('string'), 'created_at': Value('timestamp[s]'), 'total_episodes': Value('int64'), 'total_steps': Value('int64'), 'total_images': Value('int64'), 'benchmarks': List(Value('string')), 'episodes': List({'id': Value('string'), 'steps': Value('int64'), 'images': Value('int64'), 'benchmark': Value('string'), 'task': Value('string'), 'prompt': Value('string'), 'model': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              images: list<item: string>
                child 0, item: string
              tools: list<item: struct<type: string, function: struct<name: string, description: string, parameters: stru (... 287 chars omitted)
                child 0, item: struct<type: string, function: struct<name: string, description: string, parameters: struct<type: st (... 275 chars omitted)
                    child 0, type: string
                    child 1, function: struct<name: string, description: string, parameters: struct<type: string, properties: struct<comman (... 243 chars omitted)
                        child 0, name: string
                        child 1, description: string
                        child 2, parameters: struct<type: string, properties: struct<commands: struct<type: string, items: struct<type: string>,  (... 188 chars omitted)
                            child 0, type: string
                            child 1, properties: struct<commands: struct<type: string, items: struct<type: string>, description: string>, skill: stru (... 124 chars omitted)
                                child 0, commands: struct<type: string, items: struct<type: string>, description: string>
                                    child 0, type: string
                                    child 1, items: struct<type: string>
                                        child 0, type: string
                                    child 2, description: string
                                child 1, skill: struct<type: string, description: string>
                                    child 0, type: string
                                    child 1, description: string
                                child 2, target_point: struct<type: string, items: struct<type: 
              ...
              tion: struct<name: string, arguments: string>>>
                        child 0, item: struct<id: string, type: string, function: struct<name: string, arguments: string>>
                            child 0, id: string
                            child 1, type: string
                            child 2, function: struct<name: string, arguments: string>
                                child 0, name: string
                                child 1, arguments: string
                    child 4, tool_call_id: string
              meta: struct<episode: string, source: string, benchmark: string, task: string, instruction: string, total_ (... 92 chars omitted)
                child 0, episode: string
                child 1, source: string
                child 2, benchmark: string
                child 3, task: string
                child 4, instruction: string
                child 5, total_steps: int64
                child 6, n_images: int64
                child 7, supervise_turns: list<item: int64>
                    child 0, item: int64
                child 8, host_final_success: bool
              total_episodes: int64
              created_at: timestamp[s]
              benchmarks: list<item: string>
                child 0, item: string
              schema: string
              episodes: list<item: struct<id: string, steps: int64, images: int64, benchmark: string, task: string, prompt:  (... 23 chars omitted)
                child 0, item: struct<id: string, steps: int64, images: int64, benchmark: string, task: string, prompt: string, mod (... 11 chars omitted)
                    child 0, id: string
                    child 1, steps: int64
                    child 2, images: int64
                    child 3, benchmark: string
                    child 4, task: string
                    child 5, prompt: string
                    child 6, model: string
              total_steps: int64
              name: string
              total_images: int64
              to
              {'schema': Value('string'), 'name': Value('string'), 'created_at': Value('timestamp[s]'), 'total_episodes': Value('int64'), 'total_steps': Value('int64'), 'total_images': Value('int64'), 'benchmarks': List(Value('string')), 'episodes': List({'id': Value('string'), 'steps': Value('int64'), 'images': Value('int64'), 'benchmark': Value('string'), 'task': Value('string'), 'prompt': Value('string'), 'model': Value('string')})}
              because column names don't match

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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 

# 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 

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:
{
  "captain_robot_sft": {
    "file_name": "sft.jsonl",
    "formatting": "sharegpt",
    "columns": {
      "messages": "messages",
      "images": "images",
      "tools": "tools"
    }
  }
}
  1. Launch training:
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:

{
  "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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