外观
论文arXiv 2610.1107220 天前
迈向路径创造型导航：通过具身交互的机器人导航
原文链接：Towards Path-Creative Navigation: Robot Navigation through Embodied Interaction（arXiv 2610.11072 · 发布日期：2026-10-08）
Towards Path-Creative Navigation: Robot Navigation through Embodied Interaction
提出PCN及无地图视觉-LiDAR-里程计框架，在仿真和两项真实任务验证交互式导航。
核心要点
- 传统自主导航假设环境固定，只在现有自由空间找路，受阻时可能需要具身交互。
- 将PCN表述为协调运动与具身交互恢复自由空间，并提出无地图统一导航框架。
- 基于可通行性决策融合视觉与LiDAR距离，判断阻塞是否可操作及能否安全绕行。
- 在仿真和两项真实任务中，相比基线取得更高任务完成率，并支持多类交互行为。
验证范围与局限：验证范围为所设计的仿真场景和两项真实世界任务。
英文摘要 (Abstract)
Autonomous navigation in cluttered and constrained environments typically assumes a fixed environment and searches only for paths within existing free space. However, a route can be blocked by an articulated structure, a movable object, or a pedestrian, and reaching the goal may therefore require appropriate embodied interaction with the environment. This paper formulates Path-Creative Navigation (PCN), a navigation paradigm in which the robot recovers free space by coordinating locomotion and embodied interaction, and addresses one class of PCN tasks with a mapless framework. The vision-LiDAR-odometry framework integrates local observations, obstacle geometry, and robot-state estimates through a unified navigation system, providing stable semantic and geometric information for navigation and interaction. The traversability-aware decision method utilizes visual and LiDAR distance information to determine whether a blockage is actionable and whether it can be safely bypassed, enabling the robot to interact only when necessary while supporting articulated structure pushing, movable object pushing, obstacle avoidance, and pedestrian requests. We design simulation scenarios and evaluate different methods in both simulation and two real-world tasks that cannot be completed by conventional navigation alone without embodied interaction. These results demonstrate that our framework achieves higher task completion than the baseline methods by coordinating navigation and interaction in cluttered and constrained environments.
作者：Haoyu Xi, Siwei Cheng, Xiao Liu, Brenda Li, Wei Zhang
来源
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