FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments

Hu, Chuangye; Liu, Lulu; Si, Huaiwei; Zhao, Yawen; Ding, Nan

doi:10.24963/ijcai.2026/841

FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments

Chuangye Hu, Lulu Liu, Huaiwei Si, Yawen Zhao, Nan Ding

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI and Robotics. Pages 7566-7574.

https://doi.org/10.24963/ijcai.2026/841

Keywords

Robot control, planning, and execution with guarantees: Architectures connecting high-level intent and constraints to low-level trajectories
AIR: Robot control, planning, and execution with guarantees
Learning to understand, generalize, and explain actions: Robust generalization and transfer across tasks, objects, environments, embodiments, and long-horizon scenarios
Foundations of human–robot interaction and assistance: Learning and inference methods for aligning robot behavior with human instructions, demonstrations, and feedback

Abstract

Vision-and-language navigation (VLN) requires agents to follow natural language instructions to navigate autonomously in continuous environments. However, existing approaches often lack high-level semantic guidance in waypoint prediction and explicit language–landmark alignment in cross-modal planning. To address these limitations, we propose FILD-Nav, a vision-and-language navigation framework that integrates instruction landmark features. FILD-Nav extracts task-relevant landmarks from instructions and incorporates landmark semantics into both waypoint prediction and topological planning. Specifically, landmark-guided waypoint prediction improves waypoint relevance, while landmark-enhanced cross-modal planning enables more effective long-horizon navigation. Extensive experiments on the VLN-CE benchmark demonstrate that FILD-Nav consistently outperforms prior methods, achieving improvements of 2% in Success Rate (SR), 3% in Success weighted by Path Length (SPL), and 7% in Oracle Success Rate (OSR), particularly in unseen environments.

Chuangye Hu, Lulu Liu, Huaiwei Si, Yawen Zhao, and Nan Ding. 2026. FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7566–7574. https://doi.org/10.24963/ijcai.2026/841

@inproceedings{ijcai2026p841,
  title     = {FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments},
  author    = {Hu, Chuangye and Liu, Lulu and Si, Huaiwei and Zhao, Yawen and Ding, Nan},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {7566--7574},
  year      = {2026},
  month     = {8},
  note      = {AI and Robotics},
  doi       = {10.24963/ijcai.2026/841},
  url       = {https://doi.org/10.24963/ijcai.2026/841},
}

TY  - CPAPER
TI  - FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments
AU  - Hu, Chuangye
AU  - Liu, Lulu
AU  - Si, Huaiwei
AU  - Zhao, Yawen
AU  - Ding, Nan
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 7566
EP  - 7574
DO  - 10.24963/ijcai.2026/841
UR  - https://doi.org/10.24963/ijcai.2026/841
ER  -