---
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}
}
```