{"type":"ScholarlyArticle","title":"FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments","authors":[{"name":"Chuangye Hu","slug":"chuangye-hu","given":"Chuangye","family":"Hu","affiliation":""},{"name":"Lulu Liu","slug":"lulu-liu","given":"Lulu","family":"Liu","affiliation":""},{"name":"Huaiwei Si","slug":"huaiwei-si","given":"Huaiwei","family":"Si","affiliation":""},{"name":"Yawen Zhao","slug":"yawen-zhao","given":"Yawen","family":"Zhao","affiliation":""},{"name":"Nan Ding","slug":"nan-ding","given":"Nan","family":"Ding","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/841","url":"https://ijcai.org/proceedings/2026/841","pdf":"https://ijcai.org/proceedings/2026/0841.pdf","markdown":"https://ijcai.org/proceedings/2026/841.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/841","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Robotics","track":"AI and Robotics","number":841,"pages":{"first":7566,"last":7574},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}