1. Haptera
- F1
- 40.58%
- Precision
- 33.94%
- Recall
- 50.44%
- Median timing error, ms
- 80.39
How reliably can a system find physical events using only standard video?
Maintained by Haptera
Find the start of a force change across 83 REASSEMBLE recordings and 3,898 sensor-timed events. Predictions within 200 ms of the sensor time count as matches.
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| Rank / Model | F1 | Precision | Recall | Median timing error, ms |
|---|---|---|---|---|
| 1. Haptera | 40.58% | 33.94% | 50.44% | 80.39 |
| 2. Gemini 3.1 Pro Preview | 21.30% | 15.36% | 34.71% | 56.97 |
| 3. Open V-JEPA2 | 12.09% | 42.24% | 7.05% | 87.31 |
| 4. Open DINOv2 | 4.15% | 23.00% | 2.28% | 119.98 |
Identify contact that produces force in each video window. The same 83 REASSEMBLE recordings.
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| Model | Contact precision | No-contact precision | Contact recall | Coverage |
|---|---|---|---|---|
| Haptera | 92.74% | 99.72% | 51.82% | 76.25% |
| Gemini 3.1 Pro Preview | 24.62% | 79.55% | 22.93% | 99.88% |
Find sensor changes from video across three robot setups. F1 at ±200 ms, averaged equally across setups.
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| Model | F1 (mean) | Setup 2 | Setup 6 | Setup 7 |
|---|---|---|---|---|
| Haptera | 2.26% | 1.12% | 3.24% | 2.42% |
| Open DINOv2 | 0.85% | 0.67% | 0.90% | 0.98% |
| Open V-JEPA2 | 0.27% | 0.20% | 0.23% | 0.39% |
A new machine should be able to learn from interactions recorded before it existed, somewhere else, on different hardware.
Test your model with the prepared videos, labels and evaluator.
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