Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty
This work improves precision and robustness of aerial manipulation under changing operating conditions, addressing a practical bottleneck for real-world deployment.
Aerial manipulators face challenges from rapid arm motion, payload changes, and dynamic uncertainty. The proposed hierarchical framework combining RL with an inner-loop dynamics estimator reduces end-effector tracking error and improves task success rate compared to RL+PID and RL+INDI+PID baselines in hardware experiments.
Aerial manipulators enable physical interaction in hard-to-reach environments; however, the combined problem of direct whole-body aerial manipulation under rapid arm motion, payload changes, and related unknown dynamic uncertainty remains a largely unsolved problem. We present a hierarchical control framework that combines Reinforcement Learning (RL) with an inner-loop dynamics estimator to address this problem. The RL outer loop maps desired 6-degrees-of-freedom (DOF) end-effector targets to coordinated whole-body commands, enabling direct task-driven control without relying on a fully accurate coupled dynamic model in the policy layer. An inner loop then tracks these commands while compensating for transient inertial shifts and uncertainty during execution via a dynamics estimator scheme without requiring system model knowledge. We validate the proposed approach on a custom quadrotor equipped with a 3-DoF manipulator through hardware experiments under varying payload conditions. Compared with RL+PID and RL+INDI+PID baselines, the proposed method reduces end-effector tracking error and improves task success rate across the tested hardware conditions. These results show that combining learned whole-body coordination with estimator-based low-level compensation improves the precision and robustness of aerial manipulation under changing operating conditions.