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README.md

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+---
+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}
+}
+```
+
+---
+
+## 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