Flatten dataset artifacts and refresh viewer table

Upload the fixed 5,037-sample canonical PKL as root-level navi_data.pkl, regenerate the Dataset Viewer Parquet table without the folder column, and remove the nested dataset/data artifacts plus preview JSON to avoid ambiguity.

README.md

@@ -18,8 +18,8 @@ size_categories:
 configs:
 - config_name: default
   data_files:
-  - split: test
-    path: data/viewer-00000-of-00001.parquet
+  - split: viewer
+    path: viewer-00000-of-00001.parquet
 ---
 
 # EmbodiedNav-Bench
@@ -41,9 +41,8 @@ The dataset is intended for evaluating embodied navigation, spatial reasoning, a
 
 | Path | Description |
 | :-- | :-- |
-| `dataset/navi_data.pkl` | Canonical PKL file for evaluation. |
-| `dataset/navi_data_preview.json` | Human-readable JSON preview of the PKL content. |
-| `data/viewer-00000-of-00001.parquet` | Parquet representation for the Hugging Face Dataset Viewer table. |
+| `navi_data.pkl` | Canonical PKL file for evaluation. |
+| `viewer-00000-of-00001.parquet` | Parquet representation for the Hugging Face Dataset Viewer table. |
 
 ## Data Fields
 
@@ -60,13 +59,11 @@ The canonical PKL file stores a list of Python dictionaries. Each sample contain
 | `gt_traj` | `float[N,3]` | Ground-truth trajectory points. |
 | `gt_traj_len` | `float` | Ground-truth trajectory length. |
 
-The Parquet table includes the same structured fields and additional convenience columns such as `sample_index`, `start_x`, `start_y`, `start_z`, `target_x`, `target_y`, `target_z`, and `gt_traj_num_points`. It is provided for browsing and visualization in the Hugging Face Dataset Viewer.
+The Parquet table includes the same structured fields and additional convenience columns such as `sample_index`, `start_x`, `start_y`, `start_z`, `target_x`, `target_y`, `target_z`, and `gt_traj_num_points`. The `folder` field is omitted from the table because `sample_index` provides the browsing index. The Parquet file is provided for browsing and visualization in the Hugging Face Dataset Viewer.
 
 ## Usage
 
-Please refer to the github repository.
-
-<!-- The Dataset Viewer-compatible table can be loaded with the `datasets` library:
+The Dataset Viewer-compatible table can be loaded with the `datasets` library:
 
 ```python
 from datasets import load_dataset
@@ -75,11 +72,11 @@ ds = load_dataset("EmbodiedCity/EmbodiedNav-Bench", split="viewer")
 print(ds[0])
 ```
 
-For evaluation, use `dataset/navi_data.pkl` as the canonical data file and follow the setup instructions in the GitHub project repository. -->
+For evaluation, use `navi_data.pkl` as the canonical data file and follow the setup instructions in the GitHub project repository.
 
 ## License
 
-This dataset is released under the CC-BY-4.0 license.
+This dataset is released under the CC BY 4.0 license.
 
 ## Citation
 

dataset/navi_data_preview.json

@@ -1,272 +0,0 @@
-{
-  "source": "dataset/navi_data.pkl",
-  "num_samples": 5037,
-  "note": "This JSON is a human-readable preview. Ground-truth data is stored in the PKL file.",
-  "fields": {
-    "folder": "str, scene folder identifier",
-    "start_pos": "float[3], initial drone world position (x, y, z)",
-    "start_rot": "float[3], initial drone orientation (roll, pitch, yaw in radians)",
-    "start_ang": "float, initial camera gimbal angle in degrees",
-    "task_desc": "str, natural-language navigation goal description",
-    "target_pos": "float[3], target world position (x, y, z)",
-    "gt_traj": "float[N,3], ground-truth trajectory points",
-    "gt_traj_len": "float, ground-truth trajectory length"
-  },
-  "statistics": {
-    "gt_traj_len_min": 20.0,
-    "gt_traj_len_max": 1450.006205125028,
-    "gt_traj_len_mean": 158.47320929108906
-  },
-  "preview_samples": [
-    {
-      "sample_index": 0,
-      "folder": "0",
-      "task_desc": "the roof on the white house in front of you",
-      "start_pos": [
-        7644.1665,
-        -3637.45654,
-        -22.36726
-      ],
-      "start_rot": [
-        0.0,
-        0.0,
-        1.96349536
-      ],
-      "start_ang": 0.0,
-      "target_pos": [
-        7625.03223,
-        -3591.2627,
-        -12.367259
-      ],
-      "gt_traj_len": 59.999912430650184,
-      "gt_traj_num_points": 7,
-      "gt_traj_preview_first5": [
-        [
-          7644.1665,
-          -3637.45654,
-          -22.36726
-        ],
-        [
-          7640.33984,
-          -3628.21777,
-          -22.36726
-        ],
-        [
-          7636.51318,
-          -3618.979,
-          -22.36726
-        ],
-        [
-          7636.51318,
-          -3618.979,
-          -12.367259
-        ],
-        [
-          7632.68604,
-          -3609.74023,
-          -12.367259
-        ]
-      ]
-    },
-    {
-      "sample_index": 1,
-      "folder": "1",
-      "task_desc": "the nearby terrace on the 29th floor of a yellow-exterior building, with the number 29 on the exterior wall",
-      "start_pos": [
-        5775.11621,
-        -3606.19556,
-        -72.3671875
-      ],
-      "start_rot": [
-        0.0,
-        0.0,
-        2.74889337
-      ],
-      "start_ang": 0.0,
-      "target_pos": [
-        5807.15283,
-        -3582.79956,
-        -122.367188
-      ],
-      "gt_traj_len": 159.99963191243666,
-      "gt_traj_num_points": 27,
-      "gt_traj_preview_first5": [
-        [
-          5775.11621,
-          -3606.19556,
-          -72.3671875
-        ],
-        [
-          5775.11621,
-          -3606.19556,
-          -72.3671875
-        ],
-        [
-          5775.11621,
-          -3606.19556,
-          -72.3671875
-        ],
-        [
-          5775.11621,
-          -3606.19556,
-          -72.3671875
-        ],
-        [
-          5775.11621,
-          -3606.19556,
-          -82.3671875
-        ]
-      ]
-    },
-    {
-      "sample_index": 2,
-      "folder": "2",
-      "task_desc": "the area in front of and beside the black vehicle",
-      "start_pos": [
-        6558.31348,
-        -4182.68701,
-        -2.36725903
-      ],
-      "start_rot": [
-        0.0,
-        0.0,
-        -3.14159254
-      ],
-      "start_ang": 0.0,
-      "target_pos": [
-        6434.1499,
-        -4225.20166,
-        -2.36725903
-      ],
-      "gt_traj_len": 140.0004342893851,
-      "gt_traj_num_points": 22,
-      "gt_traj_preview_first5": [
-        [
-          6558.31348,
-          -4182.68701,
-          -2.36725903
-        ],
-        [
-          6548.31348,
-          -4182.68701,
-          -2.36725903
-        ],
-        [
-          6538.31348,
-          -4182.68701,
-          -2.36725903
-        ],
-        [
-          6528.31348,
-          -4182.68701,
-          -2.36725903
-        ],
-        [
-          6518.31348,
-          -4182.68701,
-          -2.36725903
-        ]
-      ]
-    },
-    {
-      "sample_index": 3,
-      "folder": "3",
-      "task_desc": "上方米色外墙的建筑的22楼的露台（外墙有标数字）",
-      "start_pos": [
-        5827.45605,
-        -3566.9043,
-        -2.3671875
-      ],
-      "start_rot": [
-        0.0,
-        0.0,
-        2.74889343
-      ],
-      "start_ang": 45.0,
-      "target_pos": [
-        5808.21729,
-        -3583.07739,
-        -92.3671875
-      ],
-      "gt_traj_len": 129.99999632428458,
-      "gt_traj_num_points": 18,
-      "gt_traj_preview_first5": [
-        [
-          5827.45605,
-          -3566.9043,
-          -2.3671875
-        ],
-        [
-          5827.45605,
-          -3566.9043,
-          -12.3671875
-        ],
-        [
-          5827.45605,
-          -3566.9043,
-          -22.3671875
-        ],
-        [
-          5827.45605,
-          -3566.9043,
-          -22.3671875
-        ],
-        [
-          5827.45605,
-          -3576.9043,
-          -22.3671875
-        ]
-      ]
-    },
-    {
-      "sample_index": 4,
-      "folder": "4",
-      "task_desc": "The pink tree in front of the gate of the building ahead",
-      "start_pos": [
-        5838.2207,
-        -3592.0459,
-        -94.9805832
-      ],
-      "start_rot": [
-        0.0,
-        0.0,
-        2.74889374
-      ],
-      "start_ang": 0.0,
-      "target_pos": [
-        5722.9585,
-        -3595.12549,
-        -14.9805775
-      ],
-      "gt_traj_len": 240.00179701808574,
-      "gt_traj_num_points": 33,
-      "gt_traj_preview_first5": [
-        [
-          5838.2207,
-          -3592.0459,
-          -94.9805832
-        ],
-        [
-          5838.2207,
-          -3592.0459,
-          -94.9805832
-        ],
-        [
-          5834.39355,
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-          -94.9805832
-        ],
-        [
-          5834.39355,
-          -3601.28467,
-          -94.9805832
-        ],
-        [
-          5827.32227,
-          -3608.35547,
-          -94.9805832
-        ]
-      ]
-    }
-  ]
-}

dataset/navi_data.pkl → navi_data.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2ea4223d5d20f23cd4e43fadb19c6d126cdd455670e7eb8be7226de305d1c201
-size 4162202
+oid sha256:890a647b570626dcd024959dbbc7ba8b463545e35c92d6baaae7313cdbfb1533
+size 4162249

data/viewer-00000-of-00001.parquet → viewer-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c05abbbc5966b23c3d71e45e7c32820af6d41cdd2a8dfb6412931a4ad6e2ec09
-size 1865037
+oid sha256:72a6eca9f27c4e611ace74f60594470c53b6dd9f6bcfb91c20b75bb6f2888692
+size 1833039