Hugging Face Trending Papers
RingNav: Task-Shaped Circular Memory for Visual Heading
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arXiv Computer Vision
Oct 15
RingNav: Task-Shaped Circular Memory for Visual Heading
arXiv:2610.19044v1 Announce Type: cross Abstract: Tiny robots have limited computation and memory, yet visual navigation systems often maintain more state than a task requires. We ask whether persist...
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
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 Machine Learning
Jun 17
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
arXiv AI
Oct 28
Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution
Retrieve to Act (R2A) is a test‑time scaling framework that improves the robustness of Vision‑Language‑Action (VLA) policies to out‑of‑distribution visual shifts. It transforms OOD robustness into a geometric motion‑primitive retrieval problem by building an offline spatiotemporal motion‑primitive graph from successful training demonstrations. During inference, a graph neural network localizes the robot’s current state, retrieves compatible future motion primitives, and blends them with the frozen VLA’s actions via a learned gating mechanism, thereby correcting erroneous spatial trajectories while preserving local reactivity.
arXiv AI
Jun 18
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
arXiv:2606. 18634v1 Announce Type: cross Abstract: To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots.