| --- |
| pretty_name: RoboDojo SFT Data for VLM Agents |
| license: cc-by-nc-4.0 |
| extra_gated_heading: Request access to RoboDojo SFT Data for VLM Agents |
| extra_gated_prompt: >- |
| This dataset is released under CC BY-NC 4.0 for non-commercial use only. By requesting access you |
| agree to: (1) use the dataset, and anything derived from it, including models trained on it, only for |
| non-commercial research or education; (2) credit this dataset when you publish or share work that uses |
| it; (3) pass these terms on to anyone you share it with. Requests are reviewed by hand. |
| extra_gated_fields: |
| Name: text |
| Affiliation: text |
| Country: country |
| Intended use: text |
| I will use this dataset only for non-commercial purposes: checkbox |
| extra_gated_button_content: Agree and request access |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.arrow |
| task_categories: |
| - robotics |
| - image-text-to-text |
| tags: |
| - robotics |
| - tool-use |
| - function-calling |
| - chain-of-thought |
| - openai-chat-format |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # RoboDojo SFT Data for VLM Agents |
|
|
| RoboDojo demonstration (3500 episodes) in multimodal agent |
| conversation in the OpenAI chat and tool call format. Each turn the robot gets its state and three camera images, then |
| writes a short first-person chain of thought and calls the tool `move`. A last turn checks |
| the task's goal in the final views and stops without a tool call. |
|
|
| | | | |
| |---|---| |
| | Conversations (rows) | 3,500, one per episode | |
| | Steps (assistant turns with a `move` call) | 89,056 | |
| | Images (embedded JPEG, 640 × 480) | 277,668 | |
| | Chain-of-thought length per assistant turn | median 92 words, 90th percentile 131, max 153 | |
| | Size | 13.4 GB in 27 Arrow shards | |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("embodied-analysis/robodojo-sft", split="train") # a local path to this folder or a Hub repo id |
| ds = load_dataset("embodied-analysis/robodojo-sft", split="train", streaming=True) # no local cache copy |
| ``` |
|
|
| `load_dataset` on local files first copies them into the Hugging Face cache (`HF_DATASETS_CACHE`). To |
| memory-map the shards in place instead: |
|
|
| ```python |
| import glob |
| from datasets import Dataset, concatenate_datasets |
| ds = concatenate_datasets([Dataset.from_file(p) for p in sorted(glob.glob("data/train-*.arrow"))]) |
| ``` |
|
|
| **Batch sizes.** A row is about 4 MB, because it holds an episode's images. Arrow caps one binary column |
| at 2 GB per batch, and `map` and `cast` write 1,000 rows per batch by default. So pass smaller batches: |
|
|
| ```python |
| ds = ds.map(fn, batch_size=32, writer_batch_size=32) |
| ``` |
|
|
| To read the stored JPEG bytes without decoding, use `ds.with_format("arrow")[i]`, or iterate with |
| `streaming=True`. |
|
|
| ## Row schema |
|
|
| | Column | Type | Meaning | |
| |---|---|---| |
| | `episode` | string | `sim_<index>` | |
| | `episode_index` | int32 | 0–3499; task t is episodes 100t … 100t + 99 | |
| | `task` | string | RoboDojo task key | |
| | `robot` | string | `ARX X5 (sim)` | |
| | `instruction` | string | the task instruction | |
| | `messages` | list of messages | the conversation (below) | |
| | `images` | list of Image | every image of the conversation, in order of appearance | |
| | `tools` | string | the OpenAI `tools` list as JSON (the `move` function and its JSON schema) | |
| | `num_steps`, `num_images` | int32 | steps (tool calls) and images in the row | |
|
|
| A message has `role`, `content`, `tool_calls` and `tool_call_id`, with one Arrow schema for every |
| message: |
| - `content` is always a list of parts `{type, text, image_url: {url}}`. |
| - A field a message doesn't have is null: `tool_calls` outside assistant turns, `text` on image parts, and |
| so on. |
| - Each `image_url.url` is `<episode>/<turn>_<camera>.jpg`. It equals the `path` of the matching entry in |
| `images`, and the k-th image part is `images[k]`. |
|
|
| The conversation: |
| 1. `system`: the system message, which describes the robot, units, frames and the `move` tool. |
| 2. `user`: `Task: <instruction>`. |
| 3. Per step: |
| - `user`: the state JSON (fingertip poses in mm, joint angles in degrees, gripper commands), then for |
| each camera a label `Camera: <name>; horizontal FOV <h> deg, vertical FOV <v> deg` and the image; |
| - `assistant`: the chain of thought, and one `move` call. Its `arguments` are JSON: |
| `{"left": {"delta_xyz": [x, y, z], "delta_rotation": [rx, ry, rz], "gripper_closed": b}, "right": {...}}`. |
| These are millimetres and a rotation vector in degrees, in the arm's current fingertip frame; |
| - `tool`: `{"state": ...}`, the measured state after the move. |
| 4. `user`, then `assistant`: the final state and views, and a completion reply with no tool call (it ends |
| the episode). |
| |
| ### Plain OpenAI JSON |
|
|
| `to_openai` drops the null fields and turns a single text part back into a plain string, which gives |
| plain OpenAI chat JSON. With `images`, it also inlines each image as a data URL: |
|
|
| ```python |
| def to_openai(messages, images=None): |
| """A row's messages as OpenAI chat JSON: null fields dropped, a content of one text part back to a string. |
| With images (the row's `images`, decoded to PIL or as {bytes, path}), each image_url becomes a data URL.""" |
| import base64 |
| import io |
| out, k = [], 0 |
| for m in messages: |
| parts = [] |
| for p in m["content"]: |
| if p["type"] == "image_url": |
| url = p["image_url"]["url"] |
| if images is not None: |
| im = images[k] |
| if isinstance(im, dict): |
| data = im["bytes"] |
| else: |
| buf = io.BytesIO() |
| im.save(buf, format="JPEG", quality=95) |
| data = buf.getvalue() |
| url = "data:image/jpeg;base64," + base64.b64encode(data).decode() |
| k += 1 |
| parts.append({"type": "image_url", "image_url": {"url": url}}) |
| else: |
| parts.append({"type": p["type"], "text": p["text"]}) |
| msg = {"role": m["role"]} |
| msg["content"] = parts[0]["text"] if len(parts) == 1 and parts[0]["type"] == "text" else parts |
| if m.get("tool_calls"): |
| msg["tool_calls"] = m["tool_calls"] |
| if m.get("tool_call_id") is not None: |
| msg["tool_call_id"] = m["tool_call_id"] |
| out.append(msg) |
| return out |
| |
| |
| import json |
| row = ds[0] |
| messages = to_openai(row["messages"]) # image_url.url stays the relative path |
| messages = to_openai(row["messages"], row["images"]) # image_url.url = data:image/jpeg;base64,... |
| tools = json.loads(row["tools"]) |
| ``` |
|
|
| ## Conventions |
|
|
| - **Fingertip frame:** origin midway between the fingertips, +X toward the fingertips, +Y across the jaws, |
| +Z = X × Y. A move is applied once, `T_goal = T_tip · T_delta`. The controller then drives the arms |
| until they arrive or stop moving, and sets the gripper after the motion. |
| - **Moves carry no step count or duration.** How long a move runs is up to the controller. |
|
|
| ## License |
|
|
| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (full text in `LICENSE`): non-commercial |
| use only. Commercial use is not permitted under this license; for a commercial license, see Contact. |
|
|
| ## Contact |
|
|
| For a commercial license, contact founders@embodiedanalysis.com. |
|
|
