外观
论文arXiv 2609.233121 个月前
在可移动障碍物中通过离散接触推动实现操作可行的导航
原文链接：Manipulation Feasible Navigation Among Movable Obstacles with Discrete Contact Pushing（arXiv 2609.23312 · 发布日期：2026-09-20）
Manipulation Feasible Navigation Among Movable Obstacles with Discrete Contact Pushing
提出分层NAMO框架，结合重定位规划、LLM依赖推断与离散接触推动，在仿真和真机验证大型不可抓障碍交互导航。
核心要点
- 仅绕行在大型可移动障碍环境中可能低效或不可行，需要同时考虑导航收益、可行放置和可执行操作。
- 高层规划从参考路径识别关键阻挡物，搜索满足几何、操作和后续导航约束的重定位方案。
- 当直接重定位受其他可移动物体阻碍时，选择性调用LLM推断辅助操作依赖，并用确定性几何规划验证。
- 通过箱形障碍表面的离散接触模式和循环强化学习策略，实现接触切换推动；项目已开源。
验证范围与局限：验证范围为仿真和真机中的绕行、单/多障碍物重定位及依赖约束场景。
英文摘要 (Abstract)
In environments with large movable obstacles, detour-only navigation can be inefficient or even infeasible, while obstacle interaction requires reasoning about navigation benefit, feasible placement, and executable manipulation. We present a hierarchical navigation among movable obstacles (NAMO) framework for mobile manipulators. At the high level, the planner identifies key blocking obstacles from reference paths and searches for relocation plans that jointly satisfy geometric, manipulation, and downstream navigation constraints. When direct relocation is hindered by other movable objects, a large language model (LLM) is selectively invoked to infer auxiliary manipulation dependencies, which are then verified by deterministic geometric planning. To execute the resulting relocation goals, we define discrete contact modes on the surfaces of box-shaped obstacles and select contact faces and regions online based on position and orientation errors, enabling straight, side, and corner pushing through contact switching. A recurrent reinforcement-learning policy coordinates the mobile base and manipulator to track tool center point (TCP) targets while preserving end-effector reachability during sustained pushing. Simulation and real-robot experiments demonstrate feasible navigation-manipulation in detour, single- and multi-obstacle relocation, and dependency-constrained scenarios, validating the framework for interactive navigation with large non-graspable obstacles. The open-source project is available at https://cloudytosunny.github.io/NAMO_DCPushing/.
作者：Shaohu Wang, Aiguo Song, Yulong Yuan, Zhongyu Sun, Tianyuan Miao, Qinjie Ji
来源
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