发表机构
Harvey Mudd College; Georgia Institute of Technology; Walton High School（哈维穆德学院; 佐治亚理工学院; 沃尔顿高中）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TAPNAV触觉主动感知框架，通过结合不确定性感知全局路径规划与信息增益驱动局部探测，使人形机器人在无视觉环境中实现导航，其状态估计误差更低、任务完成率更高。
AI 中文摘要
在视觉缺失环境中，人形机器人的导航极具挑战性，因为本体里程计会发生漂移，且定位不确定性会快速累积。本文提出TAPNAV，这是一种触觉主动感知框架，无需依赖视觉，即可使人形机器人通过主动探测周围结构实现向目标的导航。TAPNAV通过里程计、IMU和触觉接触观测维持位姿置信度，将感知不确定性感知的全局路径规划与信息增益驱动的局部探测相结合：全局规划器搜索路径时会利用触觉修正机会，将预测的定位不确定性控制在一定范围内；局部规划器选择能最大化预期信息增益的探测动作。全身控制器协调人形机器人的运动与末端执行器接触，以执行规划的导航和探测动作。我们在仿真环境及Unitree G1机器人上，针对不同平面布局和障碍物几何形状对TAPNAV进行评估，结果显示TAPNAV的状态估计误差低于基线方法，任务完成率更高。这些结果表明，主动规划与环境的物理交互可为无需视觉的人形机器人可靠导航提供定位线索。
英文摘要
Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly. We present TAPNAV, a tactile active-perception framework that enables humanoid navigation toward a goal by actively probing surrounding structures without relying on vision. TAPNAV maintains a pose belief from odometry, IMU, and tactile contact observations, and couples uncertainty-aware global route planning with information-gain-driven local probing. The global planner searches for routes that keep predicted localization uncertainty bounded by exploiting opportunities for tactile correction, while the local planner selects probe actions that maximize expected information gain. A whole-body controller coordinates the humanoid's locomotion and end-effector contact to execute the planned navigation and probe motions. We evaluate TAPNAV in simulation and on a Unitree G1 across different floor plans and obstacle geometries. TAPNAV achieves lower state estimation error and a higher task completion rate than baselines. These results demonstrate that actively planning physical interactions with the environment can provide localization cues for reliable humanoid navigation without vision.
Comments9 pages, 10 figures
