arXiv AI
ODG-NoMaD: Overhead-Camera Direction-Guided NoMaD
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arXiv Computer Vision
Sep 25
Learning to Navigate with Minimal Parameters: Decomposing Visual Navigation Through Closed-Form Geometric Interfaces
The paper introduces a compact visual navigation system that decomposes the task into three analytically‑computed geometric interfaces and three small learned modules: an egress predictor, a navigation predictor, and an endpoint‑pinned residual diffusion generator. Only 0.58 M of the 23 M parameters are trained on 44 k frames, achieving competitive success rates and the lowest collision rate among evaluated methods across 6 060 point‑goal episodes in 60 environments. The design allows further parameter reduction by replacing the frozen image encoder with a 0.54 M MobileNetV2, supports zero‑shot deployment on a Jetson Orin Nano UGV, and enables transparent failure analysis under sensor corruption.
arXiv AI
Oct 16
AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
AVALON is a topological navigation framework that enables zero‑shot indoor navigation for service robots in familiar but unmapped buildings. It relies on human‑seeded layered spatial memory, node‑verified execution, and task‑triggered human‑robot memory co‑updating, requiring only a floor plan, place clues, and an initial topology‑odometry alignment. Experiments on a Unitree Go2 robot show AVALON completing a 12.81 m mission in 46.4 s—40.9% faster than DimOS‑Go2—without any on‑site mapping.
arXiv Computer Vision
Sep 11
Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping
The paper investigates how inaccuracies in pretrained occupancy networks affect active mapping robots that select camera viewpoints to reconstruct unknown 3D scenes. By fixing the planner and varying the occupancy representation—ranging from no completion to ground‑truth occupancy—the authors find that correcting false positives or false negatives alone does not reliably improve coverage, highlighting a disconnect between occupancy accuracy and planning performance. They propose a dynamic filtering strategy that retains predictions in unexplored space while suppressing unsupported occupancy based on online observations, which preliminarily shows it can steer viewpoint selection toward reachable surfaces that would otherwise remain unseen.
arXiv Machine Learning
Jun 16
VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
arXiv:2606. 14879v1 Announce Type: cross Abstract: Mobile agents require efficient exploration strategies to map unseen environments and autonomously plan tasks.
arXiv AI
Jul 24
Robostral Navigate
arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.
arXiv AI
Sep 16
Seeing What Matters: Visual Cue Guided Video Planning for Generalizable Robot Navigation
arXiv:2609.16737v1 Announce Type: cross Abstract: Generative video models can serve as a promising backbone for robot navigation by predicting future observations as video plans. Recent approaches of...