Under review
Recently, vision-based navigation models, particularly foundation models, have demonstrated promising performance by generating viable trajectories from RGB observations alone. However, even state-of-the-art (SOTA) transformer- and diffusion-based policies struggle to generalize in unfamiliar deployment environments containing unseen obstacles or shifted conditions. As a result, generated trajectories often remain goal-directed but unsafe. Existing efforts improve safety through external trajectory correction or internal geometric priors, yet the resulting policies are not trained to explicitly represent obstacle boundaries or traversable free-space structure. To address this limitation, we propose SAFER-Nav (Segmentation-Aware Fine-tuning for Enhancing Safety of Robot Navigation), a segmentation-aware navigation model that incorporates obstacle and free-space structure directly into the policy via fine-tuning and is designed to be compatible with diverse RGB-based navigation backbones. We evaluate SAFER-Nav against SOTA models, including ViNT, NoMaD, and their CARE-augmented variants, across multiple robot platforms, diverse indoor environments, and static and dynamic obstacle scenarios, demonstrating that SAFER-Nav reduces collision frequency while maintaining goal-reaching performance.
Static obstacles evaluated on the LoCoBot platform.
Static obstacles evaluated on the RoboMaster platform.
Static obstacles evaluated on the TurtleBot4 platform.
A dynamic obstacle appears from a corner along the robot's planned path.
A dynamic obstacle approaches head-on toward the robot.
Comparison of navigation performance between baseline vision-based models and SAFER-Nav across three robot platforms and two different indoor environments. Metrics include goal arrival rate (%), collision counts per run, traveled distance (m), and completion time (s).
| Robot & Model | Environment TE | Environment CY | ||||||
|---|---|---|---|---|---|---|---|---|
| Goal% ↑ | #Coll. ↓ | Dist. (m) | Time (s) | Goal% ↑ | #Coll. ↓ | Dist. (m) | Time (s) | |
| RoboMaster | ||||||||
| SAFER-Nav (Ours) | 0.95 | 0 | 14.92 ± 0.60 | 77.31 ± 6.38 | 0.9 | 0.11 | 25.07 ± 0.49 | 129.77 ± 4.36 |
| ViNT | 0.6 | 3.17 | 14.59 ± 0.67 | 79.99 ± 6.95 | 0.4 | 0.75 | 26.20 ± 0.65 | 128.63 ± 3.12 |
| NoMaD | 0.3 | 3.67 | 16.38 ± 0.20 | 87.50 ± 5.72 | 0.5 | 3.4 | 25.48 ± 0.78 | 122.27 ± 3.70 |
| ViNT + CARE | 0.5 | 1.4 | 15.17 ± 0.62 | 82.98 ± 4.22 | 0.1 | 1 | 26.35 | 138.25 |
| NoMaD + CARE | 0.2 | 4 | 17.07 ± 1.09 | 91.75 ± 3.18 | 0.1 | 2 | 25.97 | 131.19 |
| TurtleBot4 | ||||||||
| SAFER-Nav (Ours) | 0.85 | 0.35 | 14.31 ± 0.95 | 85.83 ± 7.31 | 0.9 | 0.22 | 25.20 ± 0.80 | 137.54 ± 5.31 |
| ViNT | 0.3 | 3 | 15.21 ± 0.52 | 84.03 ± 2.28 | 0.4 | 1 | 26.12 ± 0.73 | 141.89 ± 6.90 |
| NoMaD | 0.1 | 7 | 23.42 | 120.50 | 0.2 | 4.5 | 26.33 ± 0.15 | 125.73 ± 5.49 |
| ViNT + CARE | 0.4 | 3.25 | 14.35 ± 0.28 | 85.75 ± 4.60 | 0.3 | 3.33 | 26.84 ± 0.28 | 134.37 ± 3.16 |
| NoMaD + CARE | 0.2 | 4.5 | 17.28 ± 2.31 | 107.75 ± 7.07 | 0.2 | 1.5 | 26.08 ± 0.17 | 147.95 ± 6.16 |
| LoCoBot | ||||||||
| SAFER-Nav (Ours) | 0.95 | 0.26 | 14.92 ± 0.27 | 86.08 ± 3.35 | 0.95 | 0.11 | 25.28 ± 0.87 | 133.13 ± 3.26 |
| ViNT | 0.2 | 2.5 | 17.18 ± 0.21 | 82.19 ± 2.74 | 0.6 | 0.17 | 25.88 ± 0.82 | 126.31 ± 6.09 |
| NoMaD | 0.2 | 2.5 | 18.78 ± 0.22 | 102.34 ± 1.23 | 0.4 | 4.75 | 26.14 ± 0.27 | 129.94 ± 5.83 |
| ViNT + CARE | 0 | N/A | N/A | N/A | 0.4 | 1.25 | 25.29 ± 0.14 | 142.72 ± 4.70 |
| NoMaD + CARE | 0 | N/A | N/A | N/A | 0.3 | 3 | 25.92 ± 0.49 | 152.59 ± 2.18 |
Each marker represents one method. The x-axis denotes average collision counts per run and the y-axis denotes goal arrival rate (%). Methods closer to the top-left corner indicate better overall performance.
Number of trials where collisions occur (out of 10 trials) for each dynamic obstacle scenario. The dynamic obstacle is a teleoperated TurtleBot4 mobile robot.
| Model | (i) Corner-Appear | (ii) Front-Approach |
|---|---|---|
| SAFER-Nav (Ours) | 0/10 | 0/10 |
| ViNT | 5/10 | 9/10 |
| NoMaD | 6/10 | 8/10 |
| ViNT + CARE | 3/10 | 2/10 |
| NoMaD + CARE | 2/10 | 2/10 |