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About

Jianyi Liu is a robotics researcher whose work focuses on advancing autonomous navigation in unknown and dynamic environments. His key research areas include visual-language navigation, mapless path planning, and risk-aware trajectory generation. Liu’s major contributions lie in developing novel frameworks that enable robots to explore and navigate without relying on prior maps or semantic knowledge. His most cited paper, “LFENav: LLM-Based Frontiers Exploration for Visual Semantic Navigation” (2024, 6 citations), introduces a large language model-driven approach to frontier exploration, significantly improving spatial reasoning in unfamiliar settings. In “E²BA: Environment Exploration and Backtracking Agent for Visual Language Object Navigation” (2025, 5 citations), he addresses the challenge of robot navigation in unknown environments by proposing an agent that combines exploration with backtracking, enhancing generalization and transferability. Liu’s work on “Multi-risk Aware Trajectory Planning for Car-like Robot in Highly Dynamic Environments” (2023, 3 citations) tackles safe planning amidst both dynamic and static obstacles, while “DVT-Tree: Dynamic Visible Topology Tree for Efficient Mapless Navigation in Maze Environments” (2023, 2 citations) offers a real-time solution for path planning in complex, unknown spaces. His research is highly relevant for applications in search-and-rescue, autonomous driving, and service robotics.