To address the challenges in vision-language-action (VLA) models, such as the difficulty of achieving efficient real-time performance, the high cost of pre-training, and the complexity of end-to-end fine-tuning on embodied data due to domain shift and catastrophic forgetting, Dual-System VLA models were introduced.
The development and architectural details of Dual-System VLA models are discussed in the paper.
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| Method | 1 | 2 | 3 | 4 | 5 | Avg. Len. |
|---|
| Single-System | | | | | | |
| OpenVLA | 91.3 | 77.8 | 62.0 | 52.1 | 43.5 | 3.27 |
| UniVLA | 95.5 | 85.8 | 75.4 | 66.9 | 56.5 | 3.80 |
| Seer | 94.4 | 87.2 | 79.9 | 72.2 | 64.3 | 3.98 |
| Dual-System | | | | | | |
| LCB | 73.6 | 50.2 | 28.5 | 16.0 | 9.9 | 1.78 |
| RationalVLA | 74.3 | 58.3 | 42.3 | 30.0 | 20.7 | 2.26 |
| Robodual | 94.4 | 82.7 | 72.1 | 62.4 | 54.4 | 3.66 |
| OpenHelix | 97.1 | 91.4 | 82.8 | 72.6 | 64.1 | 4.08 |
| Method | LIBERO-Spatial | LIBERO-Object | LIBERO-Goal | LIBERO-Long | Avg. |
|---|
| Single-System | | | | | |
| OpenVLA | 84.7 | 88.4 | 79.2 | 53.7 | 76.5 |
| π0 | 96.8 | 98.8 | 95.8 | 85.2 | 94.2 |
| OpenVLA-OFT | 97.6 | 98.4 | 97.9 | 94.5 | 97.1 |
| GR00T N1 | 94.4 | 97.6 | 90.6 | 93.9 | 93.9 |
| UniVLA | 96.5 | 96.8 | 95.6 | 92.0 | 95.2 |
| Seer | - | - | - | 87.7 | - |
| Dual-System | | | | | |
| DexVLA | 97.2 | 99.1 | 95.6 | - | - |
| Hume | 98.6 | 99.8 | 99.4 | 98.6 | 98.6 |