LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild
We present LiteNWM, a latent navigation world model that shares visual encoding across candidates and jointly predicts their action-conditioned future representations at multiple horizons, while a learned scorer uses these predictions to select trajectories.
Key points
- Direct visual navigation policies generate trajectories efficiently but do not explicitly evaluate their future consequences.
- Generative navigation world models provide this foresight through visual rollouts, which are costly when evaluating multiple candidates.
- In real-robot experiments in unseen indoor and outdoor environments, LiteNWM improves navigation success from 43.3% to 83.3% relative to NoMaD.
- These results demonstrate that LiteNWM can be deployed for future-aware planning and closed-loop navigation on a physical robot.
Sources (1)
- [1]LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the WildarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:29 PM
We present LiteNWM, a latent navigation world model that shares visual encoding across candidates and jointly predicts their action-conditioned future representations at multiple horizons, while a learned scorer uses these predictions to select trajectories.
Direct visual navigation policies generate trajectories efficiently but do not explicitly evaluate their future consequences.
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