Papers › Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
Samyak Datta, Oleksandr Maksymets, Judy Hoffman, Stefan Lee, Dhruv Batra, Devi Parikh
Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with idealized sensors for localization and take deterministic actions. This setting is practically sterile by comparison to the dirty reality of noisy sensors and actuations in the real world -- wheels can slip, motion sensors have error, actuations can rebound. In this work, we take a step towards this noisy reality, developing point-goal navigation agents that rely on visual estimates of egomotion under noisy action dynamics. We find these agents outperform naive adaptions of current point-goal agents to this setting as well as those incorporating classic localization baselines. Further, our model conceptually divides learning agent dynamics or odometry (where am I?) from task-specific navigation policy (where do I want to go?). This enables a seamless adaption to changing dynamics (a different robot or floor type) by simply re-calibrating the visual odometry model -- circumventing the expense of re-training of the navigation policy. Our agent was the runner-up in the PointNav track of CVPR 2020 Habitat Challenge.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Robot Navigation | Habitat 2020 Point Nav test-std | ego-localization | DISTANCE_TO_GOAL | 2.232 | #5 of 10 | Archive leaderboard | report |
| Robot Navigation | Habitat 2020 Point Nav test-std | ego-localization | SOFT_SPL | 0.586 | #5 of 10 | Archive leaderboard | report |
| Robot Navigation | Habitat 2020 Point Nav test-std | ego-localization | SPL | 0.119 | #5 of 10 | Archive leaderboard | report |
| Robot Navigation | Habitat 2020 Point Nav test-std | ego-localization | SUCCESS | 0.157 | #5 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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