[Submitted on 15 Sep 2026]
Title:LEAP: Learning Emergent Active Perception for Quadruped Navigation
View a PDF of the paper titled LEAP: Learning Emergent Active Perception for Quadruped Navigation, by \"U. Bora G\"okbakan (WILLOW) and 2 other authors
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Abstract:Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment. Learned systems typically encourage this behavior with hand-designed proxy objectives, such as coverage or curiosity bonuses, that may conflict with the task. In this work, we propose a method to learn emergent active perception (LEAP) without augmentation of the task objective. We formulate the problem of goal-oriented navigation over hazardous terrains with goals that must be discovered visually. We then propose an architecture for navigation policies with active perception, and train them on a terrain curriculum where task pressure alone leads to the emergence of gaze control. Key to this emergence, LEAP works on a gaze-invariant representation that integrates depth images into egocentric belief maps. We validate its performance in held-out evaluation scenarios, where it achieves a 92.7% success rate, compared to 74.2% for scripted or 34.5% for passive perception, and comes within 4.6 points of a privileged oracle. We validate that LEAP navigation policies, unchanged, can be directly applied to steering quadrupedal locomotion policies in physics simulation.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.17628 [cs.RO]
(or arXiv:2609.17628v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.17628
arXiv-issued DOI via DataCite
Submission history
From: U Bora Goekbakan [view email] [via CCSD proxy] [v1] Tue, 15 Sep 2026 08:05:06 UTC (1,186 KB)
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