LP-NavOA：有限感知下仿人机器人的集成局部导航与避障
LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception
AI总结：
提出LP-NavOA框架，通过射线条件感知-动作PPO运动骨干和A*教师蒸馏的循环局部规划器，在短距离部分可观测感知下实现仿人机器人避障、稀疏目标恢复和稳定全身运动，将准时到达率从38-40%提升至85-97%。
摘要：
在杂乱环境中，仿人机器人的局部导航必须联合解决避障、稀疏目标恢复以及在短距离和部分可观测感知下的稳定全身运动。显式的规划器-控制器分解会引入延迟，并可能不匹配敏捷仿人机器人的指令跟踪极限，而纯反应式控制器在障碍物遮挡后可能丢失目标。我们提出了LP-NavOA，一个用于仿人机器人的有限感知导航与避障框架。首先训练一个基于射线条件感知-动作近端策略优化（PPO）的运动骨干，该骨干使用以机器人为中心的圆形航向速度指令和共享的指令侧安全滤波器。冻结该骨干后，A*和路点教师生成轨迹用于蒸馏一个循环局部规划器，该规划器在部署时仅覆盖航向指令，保持全身策略不变。运行时，LP-NavOA使用本体感觉、短距离局部范围感知和机体坐标系下的目标方向，无需全局地图、路点流或外部规划器。在MuJoCo开放墙壁和室内布局中，蒸馏后的规划器产生避障和避障后的目标恢复，将教师校准的准时到达率从38-40%提升至85-97%，并减少了相对于仅骨干控制器的接触/碰撞密集进展。消融实验表明，动态路径塑造、教师主动数据收集和圆形指令接口对于导航效率和训练3.0米/秒的骨干至关重要。Unitree G1部署分析证明了无需连续摇杆操控的硬件可执行性。
英文摘要：
Humanoid local navigation in cluttered environments must jointly resolve obstacle avoidance, sparse-goal recovery, and stable whole-body locomotion under short-range and partially observable sensing. Explicit planner-control decompositions introduce latency and can mismatch agile humanoid command-tracking limits, while purely reactive controllers may lose the goal after obstacle occlusion. We present LP-NavOA, a limited-perception navigation and obstacle-avoidance framework for humanoid robots. A raycast-conditioned perception-action proximal policy optimization (PPO) locomotion backbone is first trained with a robot-centered circular heading-speed command and a shared command-side safety filter. With this backbone frozen, A-star and waypoint teachers generate rollouts for distilling a recurrent local planner that overwrites only the heading command at deployment, leaving the whole-body policy intact. At runtime, LP-NavOA uses proprioception, short-range local range sensing, and a body-frame goal direction, requiring no global map, waypoint stream, or external planner. In MuJoCo open-wall and indoor layouts, the distilled planner produces obstacle bypassing and post-avoidance goal recovery, raising teacher-calibrated on-time arrival from 38--40\% to 85--97\% and reducing brush/contact-heavy progress relative to a backbone-only controller. Ablations show that dynamic route shaping, teacher-active data collection, and the circular command interface are important for navigation efficiency and for training the 3.0\,m/s backbone. A Unitree G1 deployment analysis demonstrates hardware executability without continuous joystick steering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
