ZeST is a zero‑shot traversability navigation system that uses a vision‑language model (VLM) to generate stochastic measurements, which are fused into an uncertainty‑aware posterior. By avoiding real‑world data collection, ZeST offers a cost‑effective, scalable solution for predicting terrain traversability. Experiments in indoor and outdoor settings show ZeST achieving 90–100% success rates, outperforming baselines that top 60%.
arXiv:2609.17141v1 Announce Type: cross Abstract: Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain in...
Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode. Existing benchmarks usually evaluate indoor and outdoor navigation separately, and many abstract away robot execution, leaving exit finding, boundary traversal, adaptation, and kinodynamic failures underexplored.
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
arXiv:2512. 21201v3 Announce Type: replace-cross Abstract: Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots.
arXiv:2609.06476v1 Announce Type: cross Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural la...
UrbanVLA is a Vision‑Language‑Action framework designed to enable delivery robots to navigate large‑scale urban environments using long‑horizon route instructions. The model aligns noisy route waypoints with visual observations and plans trajectories, trained through a two‑stage pipeline of supervised fine‑tuning on simulated data and reinforcement fine‑tuning on mixed simulation and real‑world data. Experiments show UrbanVLA outperforms strong baselines by over 55% on the SocialNav task and demonstrates reliable real‑world navigation in large urban settings.
arXiv:2606. 17294v1 Announce Type: cross Abstract: Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse embodiments and environments.
arXiv:2611.07417v1 Announce Type: cross Abstract: Real-world environments are messy, with paths often blocked by everyday objects like chairs and toys, requiring robots to perform interactive navigat...
WildOS is a unified system that enables long‑range, open‑vocabulary object search in unstructured outdoor environments by combining safe geometric exploration with semantic visual reasoning. It constructs a sparse navigation graph for spatial memory and uses the ExploRFM vision module to score frontier nodes, predicting traversability, visual frontiers, and object similarity in real time. A particle‑filter localization method estimates distant goal positions, allowing the robot to plan toward far‑away targets, and field experiments show WildOS outperforms purely geometric or vision‑based baselines in efficiency and autonomy.
arXiv:2607. 17574v1 Announce Type: cross Abstract: Reinforcement-learning navigation policies for legged robots select actions reactively from current observations and short-term memory, with limited capacity to anticipate how moving obstacles will evolve in the near future.
FLINT is a lightweight traversability estimator that uses a 21.6‑million‑parameter backbone—38 times smaller than comparable foundation models—to predict traversability from a single RGB camera. It achieves higher accuracy on held‑out terrain probes and runs at 14.7 FPS on CPU, outperforming a deployed foundation‑model system (WildOS) on 23 of 24 field logs. In closed‑loop field trials, FLINT’s best self‑supervised head reached 99% autonomy, surpassing a human‑label‑trained baseline on the same course.
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.