Datasets:

alibustami
/

miniVLA-Nav

@@ -2,347 +2,236 @@
 license: cc-by-4.0
 task_categories:
   - robotics
-  - image-to-image
 language:
   - en
 tags:
   - robotics
   - navigation
-  - imitation-learning
-  - vision-language-action
   - isaac-sim
   - nova-carter
-  - differential-drive
   - language-conditioned
-  - behavior-cloning
-  - simulation
-  - object-approach
-  - depth
-  - segmentation
-pretty_name: MiniVLA-Nav v1
 size_categories:
   - 1K<n<10K
-multilinguality:
-  - monolingual
-source_datasets:
-  - original
 ---
 # MiniVLA-Nav v1
-**A Multi-Scene Simulation Dataset for Language-Conditioned Robot Navigation**
-<!-- > Ali Al-Bustami · Department of Robotics Engineering (Thesis Project) -->
 ---
 ## Demo
-<video src="assets/montage_all_scenes.mp4" controls width="100%">All-scenes montage</video>
-*Nova Carter navigating to named objects across all four Isaac Sim environments.*
----
-## Dataset Summary
-MiniVLA-Nav v1 is a simulation dataset for the **Language-Conditioned Object Approach (LCOA)** task: given a short natural-language instruction, an NVIDIA Nova Carter differential-drive robot must navigate to the named object and stop within 1 m. Data were collected inside four photorealistic NVIDIA Isaac Sim 5.1 environments (Office, Hospital, Full Warehouse, Warehouse with Multiple Shelves).
-Each of the **1,174 episodes** pairs a language instruction with per-timestep, synchronized multimodal observations:
-| Modality | Resolution / Shape | Format |
-|---|---|---|
-| Front RGB | 640 × 640 × 3, uint8 | PNG |
-| Metric depth | 640 × 640, float32 (metres) | NumPy |
-| Instance segmentation | 640 × 640, uint16 | PNG |
-| Continuous actions (v, ω) | T × 2, float32 | NumPy |
-| Tokenized actions (7×7) | T × 2, int16 | NumPy |
-| Robot poses (x,y,z,qw,qx,qy,qz) | T × 7, float32 | NumPy |
-All sensors operate at **60 Hz** (physics Δt = 1/60 s).
 ---
-## Supported Tasks
-- **Language-Conditioned Object Approach (LCOA)** — given a natural-language goal and front RGB-D observations, predict continuous (v, ω) or discrete 7×7 action tokens to drive a differential-drive robot within 1 m of the named object.
-- **Behaviour Cloning / Imitation Learning** — dense per-step expert labels enable direct supervised training.
-- **OOD Generalisation** — structured evaluation splits test template-paraphrase and object-category out-of-distribution robustness.
 ---
-## Multimodal Observations
-Each timestep provides synchronized RGB, metric depth (float32, metres), and instance segmentation. The composites below show RGB (left) and depth colormap (right) from a mid-episode step.
-| Office | Hospital |
-|:---:|:---:|
-| ![RGB+D office](assets/rgbd_office.png) | ![RGB+D hospital](assets/rgbd_hospital.png) |
-| Full Warehouse | Warehouse (Multi-Shelf) |
-|:---:|:---:|
-| ![RGB+D full warehouse](assets/rgbd_full_warehouse.png) | ![RGB+D warehouse shelves](assets/rgbd_warehouse_shelves.png) |
-**Depth strip** — consecutive frames from an office episode, showing depth (metres) as the robot approaches the target:
-![Depth strip office](assets/depth_strip_office.png)
 ---
-## Scenes
-Four photorealistic Isaac Sim environments, each with curated seen/held-out object categories:
-### Office
-![Contact sheet — Office](assets/contact_office.png)
-### Hospital
-![Contact sheet — Hospital](assets/contact_hospital.png)
-### Full Warehouse
-![Contact sheet — Full Warehouse](assets/contact_full_warehouse.png)
-### Warehouse (Multiple Shelves)
-![Contact sheet — Warehouse Multi-Shelf](assets/contact_warehouse_multiple_shelves.png)
-| Scene | Episodes | Seen Categories | Held-out Categories |
-|---|---|---|---|
-| Office | 281 | chair, sofa, table, monitor, plant, trash\_can | fire\_extinguisher, whiteboard |
-| Hospital | 22 | chair, trash\_can | fire\_extinguisher, whiteboard |
-| Full Warehouse | 54 | shelf, rack | barrel |
-| Warehouse (Multi-Shelf) | 68 | shelf, rack | barrel |
 ---
-## Object Categories
-12 categories total — 9 seen during training, 3 held out for OOD evaluation.
-**Seen categories:**
-| chair | monitor | table | trash can |
-|:---:|:---:|:---:|:---:|
-| ![chair](assets/sample_chair.png) | ![monitor](assets/sample_monitor.png) | ![table](assets/sample_table.png) | ![trash can](assets/sample_trash_can.png) |
-| rack | crate | shelf | barrel (OOD) |
-|:---:|:---:|:---:|:---:|
-| ![rack](assets/sample_rack.png) | ![crate](assets/sample_crate.png) | ![shelf](assets/sample_shelf.png) | ![barrel](assets/sample_barrel.png) |
-**Held-out (OOD):** fire\_extinguisher, whiteboard, barrel — appear only in `test_ood_obj` split.
 ---
-## Object Category Demo
-<video src="assets/montage_office_categories.mp4" controls width="100%">Office categories montage</video>
-*All object categories navigated to in the Office scene.*
 ---
-## Dataset Structure
 ```
-v1/
-├── dataset_meta.json            # Global metadata (scenes, camera, action space, splits)
-├── assets/                      # README visual assets
-├── splits/
-│   ├── train_id.txt             # 261 episode IDs
-│   ├── val_id.txt               #  41 episode IDs
-│   ├── test_id.txt              #  50 episode IDs
-│   ├── test_ood_obj.txt         #  37 episode IDs  (held-out object categories)
-│   └── test_ood_lang.txt        #  36 episode IDs  (paraphrase OOD templates)
-├── targets_office.yaml          # Per-scene object catalogs (3-D centroids)
-├── targets_hospital.yaml
-├── targets_full_warehouse.yaml
-├── targets_warehouse_multiple_shelves.yaml
-└── episodes/
-    └── ep_{N:06d}/
-        ├── meta.json                 # Full episode metadata
-        ├── rgb_front/{t}.png         # 640×640 RGB frame at step t
-        ├── depth_front/{t}.npy       # 640×640 float32 depth (m) at step t
-        ├── seg_front/{t}.png         # 640×640 uint16 instance segmentation at step t
-        ├── actions_continuous.npy    # (T, 2) float32 — (v_t, ω_t)
-        ├── actions_tokens.npy        # (T, 2) int16  — discretized 7×7 tokens
-        └── poses.npy                 # (T, 7) float32 — (x,y,z,qw,qx,qy,qz)
 ```
-### Episode Metadata (`meta.json`)
-Each episode's sidecar JSON records the full configuration:
 ```json
 {
-  "episode_id": "ep_000321",
-  "scene_id": "full_warehouse.usd",
-  "goal": {
-    "target_category": "crate",
-    "target_id": "crate_038",
-    "goal_position_xyz_m": [-15.08, 10.77, 2.93]
-  },
-  "instruction": {
-    "text": "Go to the crate.",
-    "template_id": "train_01"
-  },
-  "spawn": { "tier": "mid", "spawn_to_target_dist_m": 3.574 },
   "rollout": {
-    "num_steps": 219,
     "terminated_by": "success",
     "success": true,
     "collision_count": 0,
-    "final_ne_m": 0.966,
-    "trajectory_length_m": 2.61
   }
 }
 ```
 ---
-## Splits
-| Split | Episodes | Description |
-|---|---|---|
-| `train_id` | 261 | Seen objects, seen instruction templates |
-| `val_id` | 41 | Seen objects, seen templates (validation) |
-| `test_id` | 50 | Seen objects, seen templates (held-out test) |
-| `test_ood_obj` | 37 | **Held-out object categories** (fire extinguisher, whiteboard, barrel) |
-| `test_ood_lang` | 36 | **Paraphrase OOD** instruction templates |
-| **Total** | **425** | (current snapshot; full budget: 2,000) |
----
-## Language Instructions
-Instructions are generated from slot-fill templates with `{object}` and `{color}` placeholders.
-**18 training templates** (T1–T18), examples:
-- "Go to the {object}."
-- "Drive to the {object} and stop."
-- "Approach the {object}."
-- "Navigate to the {object}."
-- "Your destination is the {object}."
-**12 paraphrase-OOD templates** (O1–O12), examples:
-- "Make your way to the {object}."
-- "Proceed to the {object}."
-- "Find the {object} and come to a stop."
-- "Close in on the {object}."
-> **Note:** Color-slot templates are suppressed in v1 — all targets carry `color=unknown` because USD assets do not expose material-color attributes through a standard prim API. Active pool: 13 train + 10 paraphrase-OOD templates.
 ---
-## Task Definition
-**LCOA formulation:** Given instruction $\ell$ and observations $o_t = (I_t^\text{RGB}, D_t)$, output actions $a_t = (v_t, \omega_t)$ such that the robot stops within $r_\text{success} = 1.0$ m of the target object centroid.
-**Action space:**
-- Continuous: $(v, \omega) \in [0, 1]$ m/s × $[-1.5, 1.5]$ rad/s
-- Tokenized: each dimension quantized to 7 uniform bins → 49-token vocabulary
-**Episode termination:**
-- **Success** — within 1 m and stationary for ≥ 5 consecutive steps
-- **Collision** — stall detected (no forward progress for ≥ 16 steps near obstacle)
-- **Timeout** — 1,000 steps reached without success
-Only successful episodes are retained in the dataset.
 ---
-## Spawn Tiers
-Trajectory diversity is ensured through three distance tiers:
-| Tier | Weight | Radius |
-|---|---|---|
-| Near | 30% | 1.5–3.5 m from target |
-| Mid | 40% | 3.5–7.0 m from target |
-| Far | 30% | Global curated floor points |
-Pearson correlation between spawn distance and trajectory length: **r = 0.94**.
----
-## Expert Controller
-The data-collection expert is a proportional controller using pixel-level target visibility from the instance segmentation mask:
-- **Target visible (≥ 32 px):** angular correction from mask centroid column + depth-based speed
-- **Target not visible:** bearing-only proportional law from known goal position
-- **Obstacle avoidance:** speed clamped when depth in central foreground crop < 0.25 m
 ---
-## Rollout Statistics
-| Split | N | Mean NE (m) | Mean TL (m) | Mean Steps |
-|---|---|---|---|---|
-| train\_id | 261 | 0.967 | 2.75 | 197.6 |
-| val\_id | 41 | 0.967 | 2.83 | 205.6 |
-| test\_id | 50 | 0.966 | 2.74 | 190.6 |
-| test\_ood\_obj | 37 | 0.967 | 2.38 | 174.7 |
-| test\_ood\_lang | 36 | 0.967 | 3.07 | 229.7 |
-NE = final navigation error (distance to goal at termination). TL = trajectory length.
----
-## Collection Setup
-| Property | Value |
 |---|---|
-| Simulator | NVIDIA Isaac Sim 5.1.0-rc.19 |
-| Robot | NVIDIA Nova Carter (differential-drive) |
-| Camera | front\_hawk/right stereo camera |
-| Physics rate | 60 Hz (Δt = 1/60 s) |
-| Image resolution | 640 × 640 px |
-| Random seed | 42 |
-| Generation date | 2026-04-22 |
----
-## Loading the Dataset
-```python
-import json
-import numpy as np
-from pathlib import Path
-from PIL import Image
-root = Path("v1")
-# Load split
-with open(root / "splits" / "train_id.txt") as f:
-    train_ids = [line.strip() for line in f]
-# Load an episode
-ep_dir = root / "episodes" / train_ids[0]
-meta = json.loads((ep_dir / "meta.json").read_text())
-instruction = meta["instruction"]["text"]             # "Go to the monitor."
-actions = np.load(ep_dir / "actions_continuous.npy")  # (T, 2) float32
-tokens  = np.load(ep_dir / "actions_tokens.npy")      # (T, 2) int16
-poses   = np.load(ep_dir / "poses.npy")               # (T, 7) float32
-# Load frame t=0
-rgb   = np.array(Image.open(ep_dir / "rgb_front" / "0.png"))   # (640, 640, 3)
-depth = np.load(ep_dir / "depth_front" / "0.npy")              # (640, 640) metres
-seg   = np.array(Image.open(ep_dir / "seg_front" / "0.png"))   # (640, 640) instance IDs
-```
 ---
 ## Citation
-If you use MiniVLA-Nav v1 in your research, please cite:
 ```bibtex
-@article{albustami2026minivlanav,
-  title   = {{MiniVLA-Nav v1}: A Multi-Scene Simulation Dataset for
-             Language-Conditioned Robot Navigation},
-  author  = {Al-Bustami, Ali},
-  year    = {2026},
-  note    = {Thesis project, Department of Robotics Engineering}
 }
 ```
@@ -350,10 +239,4 @@ If you use MiniVLA-Nav v1 in your research, please cite:
 ## License
-This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
----
-## Contact
-Ali Al-Bustami — alialbustami@gmail.com

 license: cc-by-4.0
 task_categories:
   - robotics
+  - visual-question-answering
 language:
   - en
 tags:
   - robotics
   - navigation
+  - vla
   - isaac-sim
   - nova-carter
   - language-conditioned
+  - embodied-ai
 size_categories:
   - 1K<n<10K
 ---
 # MiniVLA-Nav v1
+**Language-conditioned navigation dataset for Visual Language Action (VLA) model training and evaluation**, generated entirely in NVIDIA Isaac Sim 5.1 with a Nova Carter differential-drive robot across four photo-realistic scenes.
 ---
 ## Demo
+<div align="center">
+<video controls autoplay loop muted playsinline width="720">
+  <source src="https://huggingface.co/datasets/alibustami/miniVLA-Nav/resolve/main/assets/montage_all_scenes.mp4" type="video/mp4">
+</video>
+*2×2 montage — Office · Hospital · Warehouse (Full) · Warehouse (Shelves)*
+<br><br>
+<video controls autoplay loop muted playsinline width="720">
+  <source src="https://huggingface.co/datasets/alibustami/miniVLA-Nav/resolve/main/assets/montage_office_categories.mp4" type="video/mp4">
+</video>
+*Office scene — diverse goal categories*
+</div>
 ---
+## Overview
+| Property | Value |
+|---|---|
+| **Total episodes** | 1,174 |
+| **Success rate** | 100 % (failed rollouts discarded) |
+| **Scenes** | 4 (Office, Hospital, Full Warehouse, Warehouse Shelves) |
+| **Robot** | NVIDIA Nova Carter (differential drive) |
+| **Simulator** | Isaac Sim 5.1.0 |
+| **Sensor** | 640×640 RGB + Depth + Instance Segmentation |
+| **Action space** | Linear velocity *v* ∈ [0, 1] m/s · Angular velocity *ω* ∈ [−1.5, 1.5] rad/s |
+| **Max steps / episode** | 1,000 |
+| **Success radius** | 1.0 m |
+| **License** | CC-BY 4.0 |
 ---
+## Scenes & Episode Counts
+| Scene | Episodes | Seen Categories | Held-out Categories |
+|---|---|---|---|
+| Office | 700 | chair, sofa, table, monitor, plant, trash_can | fire_extinguisher, whiteboard |
+| Hospital | 52 | chair, trash_can | fire_extinguisher, whiteboard |
+| Full Warehouse | 354 | shelf, rack | barrel |
+| Warehouse (Shelves) | 68 | shelf, rack | barrel |
 ---
+## Splits
+| Split | Episodes | Description |
+|---|---|---|
+| `train_id` | 716 | In-distribution training |
+| `val_id` | 114 | In-distribution validation |
+| `test_id` | 121 | In-distribution test |
+| `test_ood_lang` | 122 | Novel instruction templates (OOD language) |
+| `test_ood_obj` | 101 | Novel object categories (OOD objects) |
+---
+## Spawn Tiers
+| Tier | Range | Proportion |
+|---|---|---|
+| Near | 1.5 – 3.5 m | ~55 % |
+| Mid | 3.5 – 7.0 m | ~44 % |
+| Far | Global free points | ~1 % |
 ---
+## Scene Previews
+### Office
+<table><tr>
+<td><img src="assets/contact_sheets/contact_office.png" width="480"/></td>
+</tr></table>
+### Hospital
+<table><tr>
+<td><img src="assets/contact_sheets/contact_hospital.png" width="480"/></td>
+</tr></table>
+### Warehouse (Full)
+<table><tr>
+<td><img src="assets/contact_sheets/contact_full_warehouse.png" width="480"/></td>
+</tr></table>
+### Warehouse (Shelves)
+<table><tr>
+<td><img src="assets/contact_sheets/contact_warehouse_multiple_shelves.png" width="480"/></td>
+</tr></table>
 ---
+## Cinematic Captures (Multi-Camera)
+Each demo episode is recorded simultaneously from **4 static camera angles** in addition to the robot's front camera.
+| Scene | View | Video |
+|---|---|---|
+| Office | 4-up composite | [office/ep_000002/4up_cinematic.mp4](assets/cinematic/office/ep_000002/4up_cinematic.mp4) |
+| Hospital | 4-up composite | [hospital/ep_000846/4up_cinematic.mp4](assets/cinematic/hospital/ep_000846/4up_cinematic.mp4) |
+| Warehouse (Full) | 4-up composite | [full_warehouse/ep_000909/4up_cinematic.mp4](assets/cinematic/full_warehouse/ep_000909/4up_cinematic.mp4) |
+| Warehouse (Shelves) | 4-up composite | [warehouse_shelves/ep_000399/4up_cinematic.mp4](assets/cinematic/warehouse_shelves/ep_000399/4up_cinematic.mp4) |
 ---
+## Episode Structure
+Each episode is stored under `data/v1/episodes/ep_XXXXXX/`:
 ```
+ep_000001/
+├── meta.json                # Full episode metadata
+├── rgb_front/               # 640×640 RGB frames (PNG)
+│   ├── 000000.png
+│   └── ...
+├── depth_front/             # 640×640 depth maps (float32 NPY, metres)
+│   └── ...
+├── seg_front/               # Instance segmentation masks (uint16 PNG)
+│   └── ...
+├── actions_continuous.npy   # (N, 2) — [v, ω] per step
+├── actions_tokens.npy       # Tokenised action sequences
+└── poses.npy                # (N, 7) — [x, y, z, qw, qx, qy, qz]
 ```
+### `meta.json` fields
 ```json
 {
+  "episode_id": "ep_000001",
+  "scene_id": "office.usd",
+  "robot": { "name": "nova_carter", "prim_path": "/World/nova_carter" },
+  "task": { "type": "language_conditioned_object_approach", "success_radius_m": 1.0 },
+  "goal": { "target_category": "monitor", "goal_position_xyz_m": [...] },
+  "instruction": { "text": "Move toward the monitor.", "split": "train_id" },
+  "spawn": { "tier": "near", "spawn_to_target_dist_m": 1.662 },
   "rollout": {
+    "num_steps": 58,
     "terminated_by": "success",
     "success": true,
     "collision_count": 0,
+    "final_ne_m": 0.96,
+    "trajectory_length_m": 0.70
   }
 }
 ```
 ---
+## Language Templates
+**18 training templates** (e.g. `"Go to the {object}."`, `"Move toward the {color} {object}."`) and **12 OOD templates** (e.g. `"Make your way to the {object}."`, `"Park next to the {object}."`) covering a wide range of natural language phrasings.
 ---
+## Action Space
+Actions are continuous `[v, ω]` tuples discretised into a 7×7 token grid:
+- **v** ∈ [0.0, 1.0] m/s (7 bins)
+- **ω** ∈ [−1.5, 1.5] rad/s (7 bins)
 ---
+## Quick-Load Example
+```python
+import json, numpy as np
+from pathlib import Path
+EP = Path("data/v1/episodes/ep_000001")
+meta    = json.loads((EP / "meta.json").read_text())
+actions = np.load(EP / "actions_continuous.npy")   # (N, 2)
+poses   = np.load(EP / "poses.npy")                # (N, 7)
+from PIL import Image
+frame0 = Image.open(EP / "rgb_front" / "000000.png")
+print(meta["instruction"]["text"])   # "Move toward the monitor."
+print(actions.shape, poses.shape)    # (58, 2)  (58, 7)
+```
 ---
+## HuggingFace Dataset Card Assets
+Publication-quality assets are in `assets/`:
+| Path | Contents |
 |---|---|
+| `assets/videos/` | 18 trajectory videos (H.264, 15 fps) + 2 montage grids |
+| `assets/cinematic/` | 60 multi-camera videos across 12 demo episodes |
+| `assets/contact_sheets/` | 4 scene contact-sheet PNGs (4 rows × 5 key frames) |
+| `assets/frames/` | 166 individual key-frame PNGs (RGB + RGB-D pairs, depth strips) |
 ---
 ## Citation
 ```bibtex
+@dataset{albustami2026minivla,
+  author    = {Albustami, Ali},
+  title     = {{MiniVLA-Nav}: A Language-Conditioned Navigation Dataset
+               for VLA Training in Isaac Sim},
+  year      = {2026},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/datasets/alibustami/miniVLA-Nav}
 }
 ```
 ## License
+[Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/)