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This research demonstrates that efficient visual navigation can be achieved by decomposing complex tasks into small learned modules combined with analytical geometric operations. By reducing trainable parameters to just 0.58M while maintaining state-of-the-art performance and low collision rates, the study challenges the prevailing reliance on massive scale in robotics AI. The approach offers a scalable path for deploying robust navigation systems on edge devices like Jetson Xavier NX without requiring billions of training frames.
Learning to Navigate Efficiently with Only 0.58M Trainable Parameters
arXiv:2607.11029v2 Announce Type: replace Abstract: Recent progress in visual navigation has largely been driven by scale: end-to-end policies with hundreds of millions of parameters trained on billions of frames or large-scale simulated data. We ask how much of this scale a single task family actually requires, and what structure can substitute for it. We propose a decomposed navigation model in which operations with known closed-form structure, such as projective geometry, occupancy, and coordinate transforms, are computed analytically and serve as interfaces between three small learned modules: an egress predictor that grounds the episode goal as a local subgoal in the current view, a navigation predictor that estimates a goal-conditioned posterior over where trajectories travel, and an endpoint-pinned residual diffusion generator that samples trajectory shapes from this posterior. The system trains only 0.58M out of a total of 22.7M parameters, on 44k frames in under one GPU-hour, yet approaches the performance of state-of-the-art models on navigation tasks across 6060 point-goal episodes and 60 environments, while having 233$\times$ fewer trainable parameters, the lowest collision rate among all evaluated methods, and 10+ Hz inference rate on a Jetson Xavier NX. The decomposition further transfers to no-goal exploration by retraining only the 123k-parameter egress head, and its failure modes under sensor corruption are transparent and analytically correctable.
Title:Learning to Navigate Efficiently with Only 0.58M Trainable Parameters
Abstract:Recent progress in visual navigation has largely been driven by scale: end-to-end policies with hundreds of millions of parameters trained on billions of frames or large-scale simulated data. We ask how much of this scale a single task family actually requires, and what structure can substitute for it. We propose a decomposed navigation model in which operations with known closed-form structure, such as projective geometry, occupancy, and coordinate transforms, are computed analytically and serve as interfaces between three small learned modules: an egress predictor that grounds the episode goal as a local subgoal in the current view, a navigation predictor that estimates a goal-conditioned posterior over where trajectories travel, and an endpoint-pinned residual diffusion generator that samples trajectory shapes from this posterior. The system trains only 0.58M out of a total of 22.7M parameters, on 44k frames in under one GPU-hour, yet approaches the performance of state-of-the-art models on navigation tasks across 6060 point-goal episodes and 60 environments, while having 233$\times$ fewer trainable parameters, the lowest collision rate among all evaluated methods, and 10+ Hz inference rate on a Jetson Xavier NX. The decomposition further transfers to no-goal exploration by retraining only the 123k-parameter egress head, and its failure modes under sensor corruption are transparent and analytically correctable.
6 pages, 4 figures. Under review
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2607.11029 [cs.RO]
(or arXiv:2607.11029v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.11029 Focus to learn more arXiv-issued DOI via DataCite
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