| --- |
| license: apache-2.0 |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - embodied-ai |
| - vla |
| - vlm |
| - visual-navigation |
| - object-search |
| - OVON |
| - qwen |
| task_categories: |
| - robotics |
| --- |
| |
| # StopWatch navigation data |
|
|
| Project page: [https://stopwatch-nav.github.io/](https://stopwatch-nav.github.io/) |
| Code: [https://github.com/amazon-far/stopwatch](https://github.com/amazon-far/stopwatch) |
|
|
| Training trajectories for the perception and policy |
| models, plus the released ObjectNav evaluation episodes and scoring viewpoints. |
| RGB and semantic supervision are rendered locally from the recorded poses. |
| Obtain scene assets separately from their publishers. Rendering writes sparse |
| class counts, never semantic masks. See the StopWatch semantic asset instructions. |
|
|
| ## Layout |
|
|
| - `train/{ovon,mp3d,hssd}/part-*.parquet` contains training episodes. |
| - `train/{ovon,mp3d,hssd}/manifest.json` identifies the data for checkpoint resume. |
| - `eval/{ovon_val_unseen,mp3dov,hssdov}/` contains gzipped Habitat episode files. |
| - `protocol/instance_viewpoints/{hm3d,mp3d,hssd}/val/` contains scoring destinations. |
| - `vocab/` contains canonical semantic names, accepted goal classes, within-class |
| names, per-class visibility thresholds, sensor mapping tables and benchmark scoring memberships. |
|
|
| ## Training schema |
|
|
| One Parquet row describes one episode. T denotes its number of pre-action frames. |
|
|
| | Column | Type | Meaning | |
| |---|---|---| |
| | `key` | string | Episode identity and local RGB video filename | |
| | `scene_id`, `scene_dataset_config` | string | Paths under the scene root | |
| | `object_category` | string | Goal name | |
| | `category_index` | int32 | OVON goal index 0..378 or MP3D/HSSD semantic class 0..539 | |
| | `goal_category_ids` | list<int32> | Zero-based semantic goal classes | |
| | `n_frames` | int32 | T | |
| | `actions` | list<int32> | T actions, 0 STOP, 1 FORWARD, 2 LEFT, 3 RIGHT | |
| | `poses` | list<float32> | 7T values, world `[x,y,z,qw,qx,qy,qz]`, y up | |
| | `geo_dists` | list<float32> | T goal distances in metres | |
|
|
| Every official training episode is included. Users render every episode, and the |
| loader excludes STOP-invisible episodes using locally rendered semantic counts |
| and the per-class visibility thresholds under our vocabulary. |
| Parts preserve row order and |
| use 2,000-row groups except for the final group. |
|
|
| The released policy weights were trained on an earlier generation of the OVON |
| trajectories; the released OVON trajectories are the later generation used by the |
| perception model. The release applies one goal vocabulary to every source, while |
| the original training used source-specific vocabulary settings. In the original |
| policy training on OVON, only the goal's own semantic class was accepted. Training |
| these recipes therefore does not reproduce the paper's exact training population. |
|
|
| ## Evaluation |
|
|
| The populations contain 2,309 clean OVON val_unseen episodes, 1,193 MP3D-OV episodes |
| and 1,751 HSSD-OV episodes. Episode files provide scene, episode ID, start pose and |
| goal category. Instance viewpoint files provide destination positions by semantic |
| class. Success requires STOP with geodesic distance below 0.25 m to any instance |
| viewpoint of an accepted class on the Stretch protocol navmesh. |
| |
| ## Citation |
| |
| Paper: [StopWatch: Goal Grounding for Object-goal Navigation](https://arxiv.org/abs/2610.23841). |
| |
| ```bibtex |
| @article{liu2026stopwatch, |
| title={StopWatch: Goal Grounding for Object-goal Navigation}, |
| author={Liu, Xinhao and Tian, Yunsheng and Qiu, Yuheng and Abbeel, Pieter and Chen, Xi and Shi, Guanya and Feng, Chen}, |
| journal={arXiv preprint arXiv:2610.23841}, |
| year={2026} |
| } |
| ``` |
