SkipWorld: Latent Skip Planning with Distilled World Models for Efficient Visual Navigation
We propose SkipWorld, a latent skip planning framework for efficient visual navigation.
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Key points
- World-model-based visual navigation enables action planning by predicting future states under candidate trajectories.
- However, long-horizon rollout remains challenging: models are often trained with ground-truth visual histories but deployed in closed-loop prediction, and evaluating many candidates with step-by-step rollout is computationally expensive.
- This design preserves accurate trajectory evaluation while reducing redundant rollout computation.
- Experiments on RECON, HuRoN, SCAND, and TartanDrive show that, compared with NWM, SkipWorld reduces the average ATE by 4.2% and RPE by 2.7%, while decreasing planning time by 62.4% with a 2.7-fold speedup.
Sources (1)
- [1]SkipWorld: Latent Skip Planning with Distilled World Models for Efficient Visual NavigationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 10:44 AM
We propose SkipWorld, a latent skip planning framework for efficient visual navigation.
World-model-based visual navigation enables action planning by predicting future states under candidate trajectories.
Extractive summary: sentences quoted from the sources.
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