datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RMBench-preset-gemini
RMBench-preset-gemini
RMBench training episodes (9 tasks, 450 episodes, 30 fps) with dense high-level labels produced by the TACOR offline annotator:
Gemini 3.7 Flash reads each whole episode as one video clip (one sample every 25 frames) and labels every sampled frame given only
the task's subtask preset (the ordered list of subtask labels, no further task-specific guidance). Each tick carries the current
subtask, the running textual memory and the visual-memory operations… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/RMBench-preset-gemini.RMBench-preset-luna
RMBench-preset-luna
RMBench training episodes (9 tasks, 450 episodes, 30 fps) with dense high-level labels produced by the TACOR offline annotator:
GPT-5.6 Luna (gpt-5.6-luna) reads the frames of each episode sampled every 25 frames as labelled images and labels every sampled frame given only
the task's subtask preset (the ordered list of subtask labels, no further task-specific guidance). Each tick carries the current
subtask, the running textual memory and the visual-memory… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/RMBench-preset-luna.voicelabfx-presetsobject_retrieval-preset-gemini
object_retrieval-preset-gemini
Real-robot teleoperation episodes of the instance_retrieval task on a single-arm Franka Research 3 cell (80 episodes, 36,239 frames at 10 fps,
released as Myungkyu/object_retrieval) with dense high-level labels produced by the TACOR offline annotator:
Gemini 3.7 Flash reads each whole episode as one video clip (one sample every 10 frames = 1.0 s) and labels every sampled frame given only the
subtask preset of the task - the label list below… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/object_retrieval-preset-gemini.RoboDojo-preset-gemini
RoboDojo-preset-gemini
RoboDojo long-horizon episodes (8 tasks, 800 episodes, 25 fps) with dense high-level labels produced by the TACOR offline annotator:
Gemini 3.7 Flash reads each whole episode as one video clip (one sample every 25 frames) and labels every sampled frame given only the
subtask preset of the task: the label list of the task-specific context (its hybrid form, so some labels carry a coordinate slot),
without the context's boundary criteria, sequence rule or… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/RoboDojo-preset-gemini.object_classification-preset-gemini
object_classification-preset-gemini
Real-robot teleoperation episodes of the object_classification task on a single-arm Franka Research 3 cell (80 episodes, 28,569 frames at 10 fps,
released as Myungkyu/object_classification) with dense high-level labels produced by the TACOR offline annotator:
Gemini 3.7 Flash reads each whole episode as one video clip (one sample every 10 frames = 1.0 s) and labels every sampled frame given only the
subtask preset of the task - the label list… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/object_classification-preset-gemini.layout_reconstruction-preset-gemini
layout_reconstruction-preset-gemini
Real-robot teleoperation episodes of the layout_reconstruction task on a single-arm Franka Research 3 cell (80 episodes, 37,559 frames at 10 fps,
released as Myungkyu/layout_reconstruction) with dense high-level labels produced by the TACOR offline annotator:
Gemini 3.7 Flash reads each whole episode as one video clip (one sample every 10 frames = 1.0 s) and labels every sampled frame given only the
subtask preset of the task - the label list… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/layout_reconstruction-preset-gemini.movement_reversal-preset-gemini
movement_reversal-preset-gemini
Real-robot teleoperation episodes of the movement_reversal task on a single-arm Franka Research 3 cell (80 episodes, 31,543 frames at 10 fps,
released as Myungkyu/movement_reversal) with dense high-level labels produced by the TACOR offline annotator:
Gemini 3.7 Flash reads each whole episode as one video clip (one sample every 10 frames = 1.0 s) and labels every sampled frame given only the
subtask preset of the task - the label list below… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/movement_reversal-preset-gemini.real_workbench-preset-gemini
real_workbench-preset-gemini
The four tasks of the real-robot Workbench Manipulation cell in one LeRobot v3.0 dataset with dense high-level
labels, pre-processed for π0.5 co-training: 320 episodes (4 x 80), 133,910 frames at 10 fps, single-arm Franka Research 3,
two camera views stored at 224x126, actions in the delta_eef space. Same frames as Myungkyu/real_workbench,
with a per-frame subtask label in place of the task instruction.
task_index
task
episodes
frames
labelled… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/real_workbench-preset-gemini.RoboDojo-preset-luna
RoboDojo-preset-luna
RoboDojo long-horizon episodes (8 tasks, 800 episodes, 25 fps) with dense high-level labels produced by the TACOR offline annotator:
GPT-5.6 Luna (gpt-5.6-luna) reads the frames of each episode sampled every 25 frames as labelled images and labels every sampled frame given only the
subtask preset of the task: the label list of the task-specific context (its hybrid form, so some labels carry a coordinate slot),
without the context's boundary criteria… See the full description on the dataset page: https://huggingface.co/datasets/Myungkyu/RoboDojo-preset-luna.doxapad-preset-2doxapad-presetpreset-arena
Preset Arena dataset
Description
dataset.json: contains pairs of completions generated with different presets for the same prompts. The chat prompts were constructed based on SODA, whereas the instruct prompts were extracted from WizardLM_evol_instruct_70k.
votes.json: the votes given by users. Each vote contains two fields: the row number, and either "left" or "right". For instance, ["instruct", 2982, "left"] corresponds to data["instruct"][2982], where the user chose… See the full description on the dataset page: https://huggingface.co/datasets/oobabooga/preset-arena.streaming-smoke-presettleThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so_follower",
"total_episodes": 5,
"total_frames": 60,
"total_tasks": 2,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 10,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/irvinh/streaming-smoke-presettle.qwen3-tts-preset-voices
Qwen3-TTS preset voice embeddings
The 9 named speakers from Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice packaged as a small sidecar bundle usable with the -Base checkpoint.
bundle.safetensors — 9 × 2048-d bfloat16 rows, ~37 KB total
bundle.json — metadata (speaker name → spk_id, gender, supported languages)
Each row is lifted from talker.model.codec_embedding.weight in the CustomVoice checkpoint at the speaker-ID index from its config.json. With these rows, you can:
Deploy only… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen3-tts-preset-voices.training_presetspreset-examplesMahou-ST-Sampler-PresetPreset_Images_DBNicolemedico-presetconfig-presetAGG_VIS_PRESETSpreset-image-metapreset-video-metaA-Preset-I-suppose