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.
Does Peer Observation Help? Vision-Sharing Collaboration for Vision-Language Navigation
The paper introduces Co‑VLN, a minimalist, model‑agnostic framework that lets agents in Vision‑Language Navigation share structured perceptual memory when they independently visit the same locations. By exchanging observations, each agent expands its receptive field without extra exploration. Experiments on the R2R benchmark with DUET and MapGPT show that vision‑sharing significantly improves performance, suggesting a promising direction for collaborative embodied navigation.
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arXiv AI
Jun 11
CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
Hugging Face Trending Papers
Jul 22
NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation
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 Computer Vision
Sep 22
What do VLM-Based Vision-Language Navigation Models Rely on: Interpreting and Steering Policy Behavior
arXiv:2609.24576v1 Announce Type: cross Abstract: Modern Vision-Language Navigation (VLN) models rely mostly on pre-trained large Vision-Language Models (VLMs) to predict navigation actions. While th...
arXiv AI
Sep 30
Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method
arXiv:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require t...
arXiv Computer Vision
Sep 18
Navi-Agent: Unlocalized Monocular Navigation Agent
Navi-Agent is a zero‑shot Vision‑Language Navigation in Continuous Environments (VLN‑CE) agent that builds a coordinate‑free spatial state from visual observations and motion history. It represents this state as a navigation topology with nodes as visual places and edges as motion transitions, enabling observation‑based self‑localization, task progress verification, and recovery. Experiments on a zero‑shot VLN‑CE benchmark and real‑world robot platforms demonstrate that Navi‑Agent achieves state‑of‑the‑art performance among geometry‑constrained methods while remaining competitive with geometry‑based approaches.
arXiv Machine Learning
Oct 2
STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction
arXiv:2609.40245v2 Announce Type: cross Abstract: Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-...
arXiv AI
Jul 24
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
arXiv AI
Sep 17
Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation
Mem2Ego introduces a vision‑language model for embodied navigation that combines global memory with egocentric visual inputs. By adaptively retrieving task‑relevant cues from a global memory module and aligning them with local perception, the framework improves spatial reasoning and decision‑making over long horizons. The method outperforms prior state‑of‑the‑art approaches on the HSSD and HM3D benchmarks and shows strong performance on a real robot.
arXiv AI
Oct 15
Interactive Navigation Beyond Reachability: Benchmark and Agentic Framework
arXiv:2610.17089v1 Announce Type: cross Abstract: Existing embodied navigation tasks commonly assume that traversability and object states remain fixed during an episode. Yet reaching a goal may requ...
arXiv AI
Jul 7
pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI
arXiv:2602. 14401v2 Announce Type: replace-cross Abstract: Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy concerns.
arXiv Computer Vision
Oct 9
SuperNav: An Agentic Navigation System for Any Task in Any Scene
SuperNav is a navigation system that leverages a pretrained multimodal large language model (MLLM) without fine‑tuning it for navigation. The system introduces an agent harness that delegates motion execution to navigation tools while the MLLM focuses on interpreting requests, understanding scenes, and making decisions. A visual‑point interface connects the model’s decisions to motion, allowing direct destination specification and feedback‑driven revision, enabling sustained navigation across varied tasks and environments.