| # Embodied Navigation VLM-Based Evaluation |
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| Evaluates whether a generated image/video correctly performs an embodied-navigation task by asking **Gemini-2.5-Pro** to judge it against the initial scene and the destination reference. This folder provides the exact evaluation prompts; run them through any Gemini access path (Google AI Studio, Gemini API, Vertex AI, or your own client). |
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| There are **four tasks**, each with a separate **video** and **image** evaluation prompt (8 prompts total), organized one subfolder per task: |
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| | Subfolder (task) | Description | |
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| | `panoramic_view_last-mile_navigation` (task01) | Last-mile navigation in a 360° panoramic FPV scene | |
| | `top-down_view_real-world_navigation` (task02) | 2D top-down multi-floor navigation | |
| | `3D_real-world_navigation` (task03) | 3D dollhouse/cutaway real-world navigation | |
| | `simultaneous_localization_and_generation` (task04) | Split-screen: 3D navigation + synchronized 2D map | |
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| Each subfolder contains: |
| - `video_evaluation_prompt.txt` — used to evaluate generated **videos** |
| - `image_evaluation_prompt.txt` — used to evaluate generated **images** (the final navigation state) |
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| ## Evaluation Protocol |
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| For each generated output, send one user message to `gemini-2.5-pro` containing: |
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| 1. **The matching evaluation prompt** (text) — the `video_` or `image_` prompt from the task's subfolder |
| 2. **The initial scene reference** — the starting panorama / top-down map / scene image |
| 3. For object-goal levels, **the location description** text specifying the destination |
| 4. **The generated video or image** to evaluate |
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| The prompts are self-contained (criteria and output schema are inline) and contain no runtime placeholders — the references above are supplied as separate inputs. |
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| ## Evaluation Criteria |
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| The prompts score a set of **binary** metrics (`"0"`/`"1"`), returned as JSON. The exact set varies slightly by task, but generally covers: |
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| | Metric | Meaning | |
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| | `SUCCESS_SCORE(_3D)` | Robot reaches and stops on/next to the specified destination by the end | |
| | `ORACLE_SUCCESS_SCORE(_3D)` | Trajectory ever passes through / pauses inside the correct destination region | |
| | `OBJECT_SEMANTIC_SCORE` | No collisions or clipping through walls/furniture | |
| | `AGENT_CONSISTENCY_SCORE` | Motion is temporally continuous (no teleporting, pose pops, size jumps) | |
| | `SPATIAL_ALIGNMENT_SCORE` | Heading / walking direction / elevation changes stay coherent | |
| | `DESTINATION_INTEGRITY_SCORE` | Destination in the output matches the reference (position, appearance, semantics) and is unchanged | |
| | `SCENE_CHANGE_SCORE` | The environment stays static aside from expected viewpoint changes | |
| | `TRAJECTORY_ALIGNMENT_SCORE` *(task04)* | The 2D-map trajectory matches the 3D navigation path | |
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| Refer to each prompt for the precise metric list and definitions for that task. The prompts instruct Gemini to be fail-safe (assign `0` when ambiguous) and to penalize physical violations even when the goal is reached. |
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| ## Response Format |
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| Each prompt instructs Gemini to return **only a JSON object** with a `SCORES` field (string `"0"`/`"1"` per metric) plus a brief reasoning field. Parse the JSON from the response. |
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| ## Metrics |
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| `SUCCESS_SCORE(_3D)` is the primary task-success metric. For a set of generations, report: |
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| ``` |
| success_rate = (# of outputs with SUCCESS_SCORE = 1) / (total # of outputs) |
| ``` |
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| `ORACLE_SUCCESS_SCORE` gives a more lenient upper bound; the remaining metrics are physical-plausibility / fidelity diagnostics. |
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