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README.md
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---
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license: apache-2.0
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task_categories:
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- robotics
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tags:
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- LeRobot
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- language_table
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- openx
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- franka
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configs:
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- config_name: default
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data_files: data/*.parquet
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---
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Language Table (LeRobot) — Embedding-Only Release
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(DINOv3 + SigLIP2 image features; EmbeddingGemma task-text features)
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This repository packages a re-encoded variant of [IPEC-COMMUNITY/cmu_play_fusion_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/cmu_play_fusion_lerobot) where raw videos are replaced by fixed-length image embeddings, and task strings are augmented with text embeddings. All indices, splits, and semantics remain consistent with the source dataset while storage and I/O are substantially lighter. To make the dataset practical to upload/download and stream from the Hub, we also consolidated tiny per-episode Parquet files into N large Parquet shards under a single data/ folder. The file meta/sharded_index.json preserves a precise mapping from each original episode (referenced by a normalized identifier of the form data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet) to its shard path and row range, so you keep original addressing without paying the small-file tax.
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- Robot: Franka
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- Modalities kept: states, actions, timestamps, frame/episode indices, image embeddings, task-text embeddings
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- Removed:
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- observation.images.image
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- License: apache-2.0 (inherits from source)
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----------------------------------------------------------------
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Quick Stats
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From meta/info.json and meta/task_text_embeddings_info.json:
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- Episodes: 576
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- Frames: 235,922
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- Tasks (unique): 44
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- Chunks (original layout): 1 (chunks_size=1000)
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- Shards (this release): 64 Parquet files under data/ (see meta/sharded_index.json)
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- FPS: 5
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- Image embeddings (per frame):
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- observation.images.image_dinov3 → float32 [1024] (DINOv3 ViT-L/16 CLS)
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- observation.images.image_siglip2 → float32 [768] (SigLIP2-base)
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- Task-text embeddings (per unique task):
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- embedding → float32 [768] from google/embeddinggemma-300m
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- Count: 44 rows (one per task)
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Note: This is an embedding-only package. The original pixel arrays listed under “Removed” are dropped.
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----------------------------------------------------------------
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<details>
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<summary><b>Contents</b></summary>
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<pre>
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.
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|-- meta/
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| |-- info.json
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| |-- sharded_index.json
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| |-- tasks.jsonl
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| |-- episodes.jsonl
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| `-- task_text_embeddings_info.json
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|-- data/
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| |-- shard-00000-of-000NN.parquet
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| |-- shard-00001-of-000NN.parquet
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| |-- ...
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| `-- task_text_embeddings.parquet
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`-- README.md
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</pre>
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</details>
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----------------------------------------------------------------
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How This Was Generated (Reproducible Pipeline)
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1) Episode → Image Embeddings (drop pixels)
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convert_lerobot_to_embeddings_mono.py (GPU-accelerated preprocessing).
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Adds:
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- observation.images.image_dinov3 (float32[1024])
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- observation.images.image_siglip2 (float32[768])
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Removes:
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- observation.images.image
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2) Task-Text Embeddings (one row per unique task)
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build_task_text_embeddings.py with SentenceTransformer("google/embeddinggemma-300m") → data/task_text_embeddings.parquet + meta/task_text_embeddings_info.json.
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3) Data Consolidation (this release)
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All per-episode Parquets were consolidated into N large Parquet shards in one data/ folder.
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- The index meta/sharded_index.json records, for each episode, its normalized source identifier data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet, the destination shard path, and the (row_offset, num_rows) range inside that shard.
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- This preserves original addressing while making Hub sync/clone/stream far faster and more reliable.
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----------------------------------------------------------------
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Metadata (Excerpts)
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meta/task_text_embeddings_info.json
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~~~json
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{
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"model": "google/embeddinggemma-300m",
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"dimension": 768,
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"normalized": true,
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"count": 44,
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"file": "task_text_embeddings.parquet"
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}
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~~~
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meta/info.json (embedding-only + shards)
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~~~json
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{
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"codebase_version": "v2.1-embeddings-sharded",
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"robot_type": "franka",
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"total_episodes": 576,
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"total_frames": 235922,
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"total_tasks": 44,
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"total_videos": 576,
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"total_chunks": 1,
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"chunks_size": 1000,
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"fps": 5,
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"splits": {
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"train": "0:576"
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},
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"data_path": "data/shard-{shard_id:05d}-of-{num_shards:05d}.parquet",
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"features": {
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"observation.state": {
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"dtype": "float32",
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"shape": [
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8
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],
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"names": {
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"motors": [
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"motor_0",
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"motor_1",
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"motor_2",
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"motor_3",
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"motor_4",
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"motor_5",
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"motor_6",
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"gripper"
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]
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}
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},
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"action": {
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"dtype": "float32",
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"shape": [
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7
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],
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"names": {
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"motors": [
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"x",
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"y",
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"z",
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"roll",
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"pitch",
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"yaw",
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"gripper"
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]
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}
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},
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"timestamp": {
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"dtype": "float32",
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"shape": [
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1
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],
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"names": null
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},
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"frame_index": {
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"dtype": "int64",
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"shape": [
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1
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],
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"names": null
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},
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"episode_index": {
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"dtype": "int64",
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"shape": [
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1
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],
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"names": null
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},
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"index": {
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"dtype": "int64",
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"shape": [
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1
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],
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"names": null
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},
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"task_index": {
|
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"dtype": "int64",
|
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"shape": [
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1
|
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],
|
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"names": null
|
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},
|
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"observation.images.image_dinov3": {
|
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"dtype": "float32",
|
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"shape": [
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1024
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],
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"names": null
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},
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"observation.images.image_siglip2": {
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"dtype": "float32",
|
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"shape": [
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768
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],
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"names": null
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}
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},
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"video_keys": [
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"observation.images.image"
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],
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"num_shards": 64,
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"index_path": "meta/sharded_index.json"
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}
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~~~
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----------------------------------------------------------------
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Environment & Dependencies
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Python ≥ 3.9 • PyTorch ≥ 2.1 • transformers • sentence-transformers • pyarrow • tqdm • decord (and optionally av)
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----------------------------------------------------------------
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Provenance, License, and Citation
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- Source dataset: [IPEC-COMMUNITY/cmu_play_fusion_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/cmu_play_fusion_lerobot)
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- License: apache-2.0 (inherits from the source)
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- Encoders to cite:
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- facebook/dinov3-vitl16-pretrain-lvd1689m
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- google/siglip2-base-patch16-384
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- google/embeddinggemma-300m
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----------------------------------------------------------------
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Changelog
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- v2.0-embeddings-sharded — Replaced video tensors with DINOv3 + SigLIP2 features; added EmbeddingGemma task-text embeddings; consolidated per-episode Parquets into N shards with a repo-local index; preserved original indexing/splits via normalized episode identifiers.
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570 |
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571 |
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576 |
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meta/info.json
ADDED
@@ -0,0 +1,106 @@
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|
1 |
+
{
|
2 |
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|
3 |
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|
4 |
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|
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|
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7 |
+
"total_videos": 576,
|
8 |
+
"total_chunks": 1,
|
9 |
+
"chunks_size": 1000,
|
10 |
+
"fps": 5,
|
11 |
+
"splits": {
|
12 |
+
"train": "0:576"
|
13 |
+
},
|
14 |
+
"data_path": "data/shard-{shard_id:05d}-of-{num_shards:05d}.parquet",
|
15 |
+
"features": {
|
16 |
+
"observation.state": {
|
17 |
+
"dtype": "float32",
|
18 |
+
"shape": [
|
19 |
+
8
|
20 |
+
],
|
21 |
+
"names": {
|
22 |
+
"motors": [
|
23 |
+
"motor_0",
|
24 |
+
"motor_1",
|
25 |
+
"motor_2",
|
26 |
+
"motor_3",
|
27 |
+
"motor_4",
|
28 |
+
"motor_5",
|
29 |
+
"motor_6",
|
30 |
+
"gripper"
|
31 |
+
]
|
32 |
+
}
|
33 |
+
},
|
34 |
+
"action": {
|
35 |
+
"dtype": "float32",
|
36 |
+
"shape": [
|
37 |
+
7
|
38 |
+
],
|
39 |
+
"names": {
|
40 |
+
"motors": [
|
41 |
+
"x",
|
42 |
+
"y",
|
43 |
+
"z",
|
44 |
+
"roll",
|
45 |
+
"pitch",
|
46 |
+
"yaw",
|
47 |
+
"gripper"
|
48 |
+
]
|
49 |
+
}
|
50 |
+
},
|
51 |
+
"timestamp": {
|
52 |
+
"dtype": "float32",
|
53 |
+
"shape": [
|
54 |
+
1
|
55 |
+
],
|
56 |
+
"names": null
|
57 |
+
},
|
58 |
+
"frame_index": {
|
59 |
+
"dtype": "int64",
|
60 |
+
"shape": [
|
61 |
+
1
|
62 |
+
],
|
63 |
+
"names": null
|
64 |
+
},
|
65 |
+
"episode_index": {
|
66 |
+
"dtype": "int64",
|
67 |
+
"shape": [
|
68 |
+
1
|
69 |
+
],
|
70 |
+
"names": null
|
71 |
+
},
|
72 |
+
"index": {
|
73 |
+
"dtype": "int64",
|
74 |
+
"shape": [
|
75 |
+
1
|
76 |
+
],
|
77 |
+
"names": null
|
78 |
+
},
|
79 |
+
"task_index": {
|
80 |
+
"dtype": "int64",
|
81 |
+
"shape": [
|
82 |
+
1
|
83 |
+
],
|
84 |
+
"names": null
|
85 |
+
},
|
86 |
+
"observation.images.image_dinov3": {
|
87 |
+
"dtype": "float32",
|
88 |
+
"shape": [
|
89 |
+
1024
|
90 |
+
],
|
91 |
+
"names": null
|
92 |
+
},
|
93 |
+
"observation.images.image_siglip2": {
|
94 |
+
"dtype": "float32",
|
95 |
+
"shape": [
|
96 |
+
768
|
97 |
+
],
|
98 |
+
"names": null
|
99 |
+
}
|
100 |
+
},
|
101 |
+
"video_keys": [
|
102 |
+
"observation.images.image"
|
103 |
+
],
|
104 |
+
"num_shards": 64,
|
105 |
+
"index_path": "meta/sharded_index.json"
|
106 |
+
}
|
meta/sharded_index.json
ADDED
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|
|
meta/task_text_embeddings_info.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"model": "google/embeddinggemma-300m",
|
3 |
+
"dimension": 768,
|
4 |
+
"normalized": true,
|
5 |
+
"count": 44,
|
6 |
+
"file": "task_text_embeddings.parquet"
|
7 |
+
}
|
meta/tasks.jsonl
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{"task_index": 0, "task": "pick pot and place in sink"}
|
2 |
+
{"task_index": 1, "task": "pick yellow bowl and place in dish rack"}
|
3 |
+
{"task_index": 2, "task": "pick radish and place in red cup"}
|
4 |
+
{"task_index": 3, "task": "open oven, then pick pineapple and place insid oven"}
|
5 |
+
{"task_index": 4, "task": "pick yellow cup and place in sink"}
|
6 |
+
{"task_index": 5, "task": "pick banana and place in blue plate"}
|
7 |
+
{"task_index": 6, "task": "open oven, then pick steak and place insid oven"}
|
8 |
+
{"task_index": 7, "task": "pick yellow bowl and place in sink"}
|
9 |
+
{"task_index": 8, "task": "pick pot and place on wooden hanger"}
|
10 |
+
{"task_index": 9, "task": "pick knife and place in pink bowl"}
|
11 |
+
{"task_index": 10, "task": "open pot, then pick bread and place insid pot"}
|
12 |
+
{"task_index": 11, "task": "open pot, then pick pineapple and place insid pot"}
|
13 |
+
{"task_index": 12, "task": "pick yellow bowl and place on wooden hanger"}
|
14 |
+
{"task_index": 13, "task": "pick banana and place in pink bowl"}
|
15 |
+
{"task_index": 14, "task": "open oven, then pick chicken and place insid oven"}
|
16 |
+
{"task_index": 15, "task": "pick orange cup and place on wooden hanger"}
|
17 |
+
{"task_index": 16, "task": "pick knife and place in blue plate"}
|
18 |
+
{"task_index": 17, "task": "pick yellow cup and place in dish rack"}
|
19 |
+
{"task_index": 18, "task": "pick carrot and place in blue plate"}
|
20 |
+
{"task_index": 19, "task": "pick orange cup and place in dish rack"}
|
21 |
+
{"task_index": 20, "task": "open grill, then pick carrot and place insid grill"}
|
22 |
+
{"task_index": 21, "task": "open pot, then pick chicken and place insid pot"}
|
23 |
+
{"task_index": 22, "task": "pick yellow cup and place on wooden hanger"}
|
24 |
+
{"task_index": 23, "task": "pick radish and place in pink bowl"}
|
25 |
+
{"task_index": 24, "task": "pick pot and place in dish rack"}
|
26 |
+
{"task_index": 25, "task": "open grill, then pick pineapple and place insid grill"}
|
27 |
+
{"task_index": 26, "task": "pick orange cup and place in sink"}
|
28 |
+
{"task_index": 27, "task": "pick spoon and place in pink bowl"}
|
29 |
+
{"task_index": 28, "task": "pick radish and place in blue plate"}
|
30 |
+
{"task_index": 29, "task": "pick knife and place in red cup"}
|
31 |
+
{"task_index": 30, "task": "open pot, then pick carrot and place insid pot"}
|
32 |
+
{"task_index": 31, "task": "open oven, then pick bread and place insid oven"}
|
33 |
+
{"task_index": 32, "task": "open grill, then pick chicken and place insid grill"}
|
34 |
+
{"task_index": 33, "task": "pick spoon and place in red cup"}
|
35 |
+
{"task_index": 34, "task": "open oven, then pick carrot and place insid oven"}
|
36 |
+
{"task_index": 35, "task": "pick banana and place in red cup"}
|
37 |
+
{"task_index": 36, "task": "pick carrot and place in red cup"}
|
38 |
+
{"task_index": 37, "task": "pick spoon and place in blue plate"}
|
39 |
+
{"task_index": 38, "task": "pick carrot and place in pink bowl"}
|
40 |
+
{"task_index": 39, "task": "open grill, then pick steak and place insid grill"}
|
41 |
+
{"task_index": 40, "task": "open grill, then pick bread and place insid grill"}
|
42 |
+
{"task_index": 41, "task": "pick red mug and place on wooden hanger"}
|
43 |
+
{"task_index": 42, "task": "open pot, then pick steak and place insid pot"}
|
44 |
+
{"task_index": 43, "task": "pick red mug and place in dish rack"}
|