LiteNWM: Efficient Latent World Models for Onboard Visual Na
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild. LiteNWM is a latent navigation world model that enables efficient visual navigation by sharing visual encoding across candidates and jointly predicting future representations. It achieves a 17.56% reduction in trajectory…
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Reported quantity · end-to-end speedup: 128 other · Basis not reported
“predicts their action-conditioned future representations at multiple horizons, while a learned scorer uses these predictions to select trajectories. In offline evaluations on RECON, SCAND, and SACSoN, LiteNWM reduces macro-averaged trajectory error by 17.56% relative to NoMaD+NWM-XL and achieves a 128.00-fold end-to-end speedup on an RTX 5090. The same evaluator transfers from NoMaD to MBRA without proposer-specific”
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- arXiv Robotics — research abstractsLiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild
LiteNWM is a latent navigation world model that enables efficient visual navigation by sharing visual encoding across candidates and jointly predicting future representations. It achieves a 17.56% reduction in trajectory error and a 128.00-fold speedup on an RTX 5090 compared to NoMaD+NWM-XL.
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