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Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space
Durgakant Pushp, Weizhe Chen, Zheng Chen +3
Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings,…
Context-Generative Default Policy for Bounded Rational Agent
Durgakant Pushp, Junhong Xu, Zheng Chen +1
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the default policy,' based on previous…
Visual-Geometry GP-based Navigable Space for Autonomous Navigation
Mahmoud Ali, Durgkant Pushp, Zheng Chen +1
Autonomous navigation in unknown environments is challenging and demands the consideration of both geometric and semantic information in order to parse the navigability of the envi…
POVNav: A Pareto-Optimal Mapless Visual Navigator
Durgakant Pushp, Zheng Chen, Chaomin Luo +2
Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailabl…
CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation
Zheng Chen, Durgakant Pushp, Lantao Liu
Traversability prediction is a fundamental perception capability for autonomous navigation. The diversity of data in different domains imposes significant gaps to the prediction pe…
NSS-VAEs: Generative Scene Decomposition for Visual Navigable Space Construction
Zheng Chen, Lantao Liu
Detecting navigable space is the first and also a critical step for successful robot navigation. In this work, we treat the visual navigable space segmentation as a scene decomposi…