野外空中操纵：机载感知、策略学习与全身控制
Aerial Manipulation in the Wild with Onboard Perception, Policy Learning, and Whole-Body Control
- Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对户外空中操纵的挑战，提出集成模仿学习、机载激光雷达-惯性状态估计和全身MPC的框架，实验验证了其在物理平台上的有效性。
AI中文摘要：
户外环境中的空中操纵由于同时需要可靠的状态估计、稳定的空中运动以及在外部扰动下的精确操纵而仍然具有挑战性。在这项工作中，我们提出了一个真实的户外空中操纵框架，该框架集成了模仿学习、机载激光雷达-惯性状态估计和全身模型预测控制。训练了一个扩散策略（Diffusion Policy），从操纵演示中学习，以根据机载观测生成期望的末端执行器运动。这些学习到的命令由全身模型预测控制（MPC）执行，该控制联合协调空中平台和机械臂以实现期望的末端执行器轨迹。为了消除对外部运动捕捉基础设施的依赖，平台在户外操作期间采用机载激光雷达-惯性里程计进行状态估计。我们在物理空中操纵器上验证了完整框架，并成功执行了户外操纵任务。实验结果表明，演示驱动的操纵策略可以与机载状态估计和基于模型的全身控制有效集成，从而实现超越受控室内环境的空中操纵。
英文摘要：
Aerial manipulation in outdoor environments remains challenging due to the simultaneous requirements of reliable state estimation, stable aerial motion, and precise manipulation under external disturbances. In this work, we present a real-world outdoor aerial manipulation framework that integrates imitation learning, onboard LiDAR-inertial state estimation, and whole-body model predictive control. A Diffusion Policy is trained from manipulation demonstrations to generate desired end-effector motions from onboard observations. These learned commands are executed by a whole-body MPC that jointly coordinates the aerial platform and manipulator to realize the desired end-effector trajectory. To eliminate reliance on external motion-capture infrastructure, the platform employs onboard LiDAR-inertial odometry for state estimation during outdoor operation. We validate the complete framework on a physical aerial manipulator and demonstrate successful execution of outdoor manipulation tasks. The experimental results show that demonstration-driven manipulation policies can be effectively integrated with onboard state estimation and model-based whole-body control to enable aerial manipulation beyond controlled indoor environments.
