VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs

• URL: http://arxiv.org/abs/2512.22342v4
• Date: Fri, 23 Jan 2026 09:50:24 GMT
• Title: VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs
• Abstract summary: Vision Language-Language Navigation (VL-LN) benchmark provides a large-scale, automatically generated dataset for training and assessing dialog-enabled navigation models.<n>VL-LN comprises over 41k long-horizon dialog-augmented trajectories for training and an automatic evaluation protocol with an oracle capable of responding to agent queries.
• Score: 51.18508300818979
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In most existing embodied navigation tasks, instructions are well-defined and unambiguous, such as instruction following and object searching. Under this idealized setting, agents are required solely to produce effective navigation outputs conditioned on vision and language inputs. However, real-world navigation instructions are often vague and ambiguous, requiring the agent to resolve uncertainty and infer user intent through active dialog. To address this gap, we propose Interactive Instance Goal Navigation (IIGN), a task that requires agents not only to generate navigation actions but also to produce language outputs via active dialog, thereby aligning more closely with practical settings. IIGN extends Instance Goal Navigation (IGN) by allowing agents to freely consult an oracle in natural language while navigating. Building on this task, we present the Vision Language-Language Navigation (VL-LN) benchmark, which provides a large-scale, automatically generated dataset and a comprehensive evaluation protocol for training and assessing dialog-enabled navigation models. VL-LN comprises over 41k long-horizon dialog-augmented trajectories for training and an automatic evaluation protocol with an oracle capable of responding to agent queries. Using this benchmark, we train a navigation model equipped with dialog capabilities and show that it achieves significant improvements over the baselines. Extensive experiments and analyses further demonstrate the effectiveness and reliability of VL-LN for advancing research on dialog-enabled embodied navigation. Code and dataset: https://0309hws.github.io/VL-LN.github.io/

Related papers

• SAIN: Structure-Aware Interactive Navigation with Active Dialogue Grounding for Mobile Robot [10.02850919496088]
  Real-world robots often encounter natural human instructions that are ambiguous, underspecified, or incomplete.<n>Interactive Instance Goal Navigation (IIGN) requires an embodied agent to find the specific instance under an ambiguous category-level instruction.<n>We present SAIN, a zero-shot framework that turns active dialogue into persistent navigation state.
  arXiv  Detail & Related papers  (2026-08-10T07:08:44Z)
• AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation [78.80536515102305]
  Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment.<n>We propose AwareVLN, a novel framework that equips the navigation model with a self-aware reasoning mechanism.<n>Our approach features two key innovations: (1) a structural reasoning module that fosters spatial and task-oriented self-awareness, and (2) an automatic data engine with progress division for effective training.
  arXiv  Detail & Related papers  (2026-05-21T17:58:26Z)
• FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation [20.13280678867453]
  FineCog-Nav organizes navigation into fine-grained modules for language processing, perception, attention, memory, imagination, reasoning, and decision-making.<n>FineCog-Nav consistently outperforms zero-shot baselines in instruction adherence, long-horizon planning, and generalization to unseen environments.
  arXiv  Detail & Related papers  (2026-04-17T17:59:48Z)
• AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild [62.47761809929869]
  Vision-language navigation (VLN) requires intelligent agents to navigate environments by interpreting linguistic instructions alongside visual observations.<n>Current VLN research for unmanned aerial vehicles (UAVs) relies on detailed, pre-specified instructions to guide the UAV along predetermined routes.<n>We propose AutoFly, an end-to-end Vision-Language-Action model for autonomous UAV navigation.
  arXiv  Detail & Related papers  (2026-02-10T11:08:07Z)
• CoINS: Counterfactual Interactive Navigation via Skill-Aware VLM [13.352875026471445]
  We propose Counterfactual Interactive Navigation via Skill-aware VLM (CoINS)<n>We fine-tune a VLM, named InterNav-VLM, which incorporates skill affordance and concrete constraint parameters into the input context.<n>To execute the generated high-level plans, we develop a comprehensive skill library through reinforcement learning.
  arXiv  Detail & Related papers  (2026-01-07T14:10:46Z)
• I2EDL: Interactive Instruction Error Detection and Localization [65.25839671641218]
  We propose a novel task of Interactive VLN in Continuous Environments (IVLN-CE) It allows the agent to interact with the user during the VLN-CE navigation to verify any doubts regarding the instruction errors. We leverage a pre-trained module to detect instruction errors and pinpoint them in the instruction by cross-referencing the textual input and past observations.
  arXiv  Detail & Related papers  (2024-06-07T16:52:57Z)
• NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models [17.495162643127003]
  We introduce the NavGPT to reveal the reasoning capability of GPT models in complex embodied scenes. NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status. We show that NavGPT is capable of generating high-quality navigational instructions from observations and actions along a path.
  arXiv  Detail & Related papers  (2023-05-26T14:41:06Z)
• AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments [60.98664330268192]
  We present AVLEN -- an interactive agent for Audio-Visual-Language Embodied Navigation. The goal of AVLEN is to localize an audio event via navigating the 3D visual world. To realize these abilities, AVLEN uses a multimodal hierarchical reinforcement learning backbone.
  arXiv  Detail & Related papers  (2022-10-14T16:35:06Z)
• LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action [76.71101507291473]
  We present a system, LM-Nav, for robotic navigation that enjoys the benefits of training on unannotated large datasets of trajectories. We show that such a system can be constructed entirely out of pre-trained models for navigation (ViNG), image-language association (CLIP), and language modeling (GPT-3), without requiring any fine-tuning or language-annotated robot data.
  arXiv  Detail & Related papers  (2022-07-10T10:41:50Z)
• VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator [41.060371177425175]
  Interactive robots navigating photo-realistic environments face challenges underlying vision-and-language navigation (VLN) We present VISITRON, a navigator better suited to the interactive regime inherent to CVDN. We perform extensive ablations with VISITRON to gain empirical insights and improve performance on CVDN.
  arXiv  Detail & Related papers  (2021-05-25T00:21:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.