Link dedicated evaluation release from training dataset README

README.md

@@ -1,86 +1,100 @@
----
-license: cc-by-nc-4.0
-language:
-- en
-task_categories:
-- robotics
-tags:
-- libero
-- libero-pro2
-- robot-learning
-- imitation-learning
-- vision-language-action
-- hdf5
-pretty_name: LIBERO-Pro2.0 Native HDF5 Training Data
-size_categories:
-- 10K<n<100K
-configs:
-- config_name: episodes
-  default: true
-  data_files:
-  - split: train
-    path: viewer/episodes/*/*.parquet
-- config_name: libero_10
-  data_files:
-  - split: train
-    path: viewer/episodes/libero_10/*.parquet
-- config_name: libero_goal
-  data_files:
-  - split: train
-    path: viewer/episodes/libero_goal/*.parquet
-- config_name: libero_object
-  data_files:
-  - split: train
-    path: viewer/episodes/libero_object/*.parquet
-- config_name: libero_spatial
-  data_files:
-  - split: train
-    path: viewer/episodes/libero_spatial/*.parquet
-- config_name: review_videos
-  data_files:
-  - split: train
-    path: viewer/review_videos/*.parquet
----
-
-# LIBERO-Pro2.0 Training Data
-
-Robot manipulation demonstrations in **native HDF5 format** for training imitation-learning and vision-language-action policies. The data covers LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10.
-
-- **40,470 stored demonstrations** across 32 base tasks and 1,349 task/variation instances.
-- Language instructions, two **128 × 128 RGB camera views**, robot states, and **7-dimensional actions**.
-- Training data only; no validation or test split.
-
-[Preview trajectories](https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro2.0-Train/viewer/episodes/train) · [Watch example videos](https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro2.0-Train/viewer/review_videos/train)
-
-## Download
-
-```bash
-pip install -U huggingface_hub h5py
-
-hf download zhouxueyang/LIBERO-Pro2.0-Train \
-  --repo-type dataset --include "data/**" \
-  --local-dir ./LIBERO-Pro2.0-Train
-```
-
-The training files require approximately **220 GiB**, plus cache space. For one suite, replace `data/**` with `data/libero_spatial/**`.
-
-## Read a demonstration
-
-Each `episodes.hdf5` file contains 30 demonstrations under `data/demo_0` through `data/demo_29`.
-
-```python
-import h5py
-
-path = "LIBERO-Pro2.0-Train/data/libero_spatial/task_1/BASE/episodes.hdf5"
-with h5py.File(path, "r") as f:
-    demo = f["data/demo_0"]
-    actions = demo["actions"][:]                         # (T, 7)
-    scene_images = demo["obs/agentview_image"][:]         # (T, 128, 128, 3)
-    wrist_images = demo["obs/robot0_eye_in_hand_image"][:] # (T, 128, 128, 3)
-```
-
-Use these sequences with your policy's training loader. See the [data format guide](docs/data_format.md) for robot-state fields, instruction metadata, and action semantics.
-
-## License
-
-[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
+---
+license: cc-by-nc-4.0
+language:
+- en
+task_categories:
+- robotics
+tags:
+- libero
+- libero-pro2
+- robot-learning
+- imitation-learning
+- vision-language-action
+- hdf5
+pretty_name: LIBERO-Pro2.0 Native HDF5 Training Data
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: episodes
+  default: true
+  data_files:
+  - split: train
+    path: viewer/episodes/*/*.parquet
+- config_name: libero_10
+  data_files:
+  - split: train
+    path: viewer/episodes/libero_10/*.parquet
+- config_name: libero_goal
+  data_files:
+  - split: train
+    path: viewer/episodes/libero_goal/*.parquet
+- config_name: libero_object
+  data_files:
+  - split: train
+    path: viewer/episodes/libero_object/*.parquet
+- config_name: libero_spatial
+  data_files:
+  - split: train
+    path: viewer/episodes/libero_spatial/*.parquet
+- config_name: review_videos
+  data_files:
+  - split: train
+    path: viewer/review_videos/*.parquet
+---
+
+# LIBERO-Pro2.0 Training Data
+
+Robot manipulation demonstrations in **native HDF5 format** for training imitation-learning and vision-language-action policies. The data covers LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10.
+
+- **40,470 stored demonstrations** across 32 base tasks and 1,349 task/variation instances.
+- Language instructions, two **128 × 128 RGB camera views**, robot states, and **7-dimensional actions**.
+- Training data only; no validation or test split.
+
+[Preview trajectories](https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro2.0-Train/viewer/episodes/train) · [Watch example videos](https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro2.0-Train/viewer/review_videos/train)
+
+## Training and Evaluation Repositories
+
+| Repository | Purpose | Download |
+|---|---|---|
+| **[LIBERO-Pro2.0-Train](https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro2.0-Train)** (this repository) | Train or fine-tune policies | HDF5 demonstrations, training metadata, training review videos and Viewer previews for 32 training tasks. |
+| [LIBERO-Pro2.0-Eval](https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro2.0-Eval) | Evaluate policies | `evaluation.zip` (evaluation code, BDDL and initial states), `assets.zip` (simulator assets), manifests and installation/verification scripts. |
+| [GitHub main](https://github.com/Zxy-MLlab/LIBERO-Pro2.0/tree/main) | Maintained source | Evaluation development, task catalogue, model adapter contract and documentation. |
+
+**For evaluation, use the evaluation repository.** Training HDF5, successful training review clips and Viewer previews are not required to evaluate a pretrained model; those previews are not held-out evaluation results. The evaluation release provides continuous batch execution, resume and per-episode success exports. Check the evaluation repository's README and open release PR for publication status before downloading.
+
+The repository split is by purpose. The benchmark's held-out split is by base task: all variants remain together. Zero-based evaluation task IDs are Spatial 0/8, Object 1/8, Goal 3/6 and LIBERO-10 5/8; this training collection contains the complementary 32 base tasks. The authoritative split is [`benchmark_split_v1.json`](https://github.com/Zxy-MLlab/LIBERO-Pro2.0/blob/main/assets/benchmark_split_v1.json).
+
+The previously published `assets.zip` and asset verification files still exist here for compatibility. The dedicated evaluation repository is their intended download destination; this README update does not delete or move existing files.
+
+## Download
+
+```bash
+pip install -U huggingface_hub h5py
+
+hf download zhouxueyang/LIBERO-Pro2.0-Train \
+  --repo-type dataset --include "data/**" \
+  --local-dir ./LIBERO-Pro2.0-Train
+```
+
+The training files require approximately **220 GiB**, plus cache space. For one suite, replace `data/**` with `data/libero_spatial/**`.
+
+## Read a demonstration
+
+Each `episodes.hdf5` file contains 30 demonstrations under `data/demo_0` through `data/demo_29`.
+
+```python
+import h5py
+
+path = "LIBERO-Pro2.0-Train/data/libero_spatial/task_1/BASE/episodes.hdf5"
+with h5py.File(path, "r") as f:
+    demo = f["data/demo_0"]
+    actions = demo["actions"][:]                         # (T, 7)
+    scene_images = demo["obs/agentview_image"][:]         # (T, 128, 128, 3)
+    wrist_images = demo["obs/robot0_eye_in_hand_image"][:] # (T, 128, 128, 3)
+```
+
+Use these sequences with your policy's training loader. See the [data format guide](docs/data_format.md) for robot-state fields, instruction metadata, and action semantics.
+
+## License
+
+[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).