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.
Figures & tables
Fig. 1 : System Overview
Fig. 2 : Indoor peg-in-hole execution in the training environment, showing the initial configuration, target approach, and successful insertion. Insets show the onboard first person view used by the visuomotor policy.
Fig. 3 : Outdoor peg-in-hole execution in an unseen environment using the same learned diffusion policy, with onboard LiDAR-inertial odometry for state estimation. Insets show the onboard first person view used by the visuomotor policy.
Environment
Norm. EE Error
Max. Base Tilt [deg]
Indoor
0.102
3.32
Outdoor
0.104
1.84
TABLE I : End-effector tracking and base attitude performance in indoor and outdoor experiments.
Fig. 4 : End-effector position tracking performance in both inddor and outdoor environments. Solid lines denote the measured end-effector position, while dashed lines denote the policy-generated reference trajectory.
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.
This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delivery. Unlike prior imitation learning-based approaches that rely on human demonstrations and are often brittle to disturbances, AdaptManip aims to train a robust loco-manipulation policy via reinforcement learning without human demonstrations or teleoperation data. The proposed framework consists of three coupled components: (1) a recurrent object state estimator that tracks the manipulated object in real time under limited field-of-view and occlusions; (2) a whole-body base policy for robust locomotion with residual manipulation control for stable object lifting and delivery; and (3) a LiDAR-based robot global position estimator that provides drift-robust localization. All components are trained in simulation using reinforcement learning and deployed on real hardware in a zero-shot manner. Experimental results show that AdaptManip significantly outperforms baseline methods, including imitation learning-based approaches, in adaptability and overall success rate, while the learned estimator keeps tracking the object when visual observations are intermittent. We further demonstrate fully autonomous real-world navigation, object lifting, and delivery on a humanoid robot.
Morgan Byrd, Donghoon Baek, Kartik Garg +5
Georgia Institute of Technology, Atlanta, GA, 30308, USA · Georgia Tech Research Institute, Atlanta, GA, 30308, USA
Vision-Language-Action (VLA) models such as π0 have demonstrated remarkable generalization across diverse fixed-base manipulators. However, transferring these foundation models to aerial platforms remains an open challenge due to the fundamental mismatch between the quasi-static dynamics of fixed-base arms and the underactuated, highly dynamic nature of flight. In this work, we introduce AirVLA, a system that investigates the transferability of manipulation-pretrained VLAs to aerial pick-and-place tasks. We find that while visual representations transfer effectively, the specific control dynamics required for flight do not. To bridge this "dynamics gap" without retraining the foundation model, we introduce a Payload-Aware Guidance mechanism that injects payload constraints directly into the policy's flow-matching sampling process. To overcome data scarcity, we further utilize a Gaussian Splatting pipeline to synthesize navigation training data. We evaluate our method through a cumulative 460 real-world experiments which demonstrate that this synthetic data is a key enabler of performance, unlocking 100% success in navigation tasks where directly fine-tuning on teleoperation data alone attains 81% success. Our inference-time intervention, Payload-Aware Guidance, increases real-world pick-and-place task success from 23% to 50%. Finally, we evaluate the model on a long-horizon compositional task, achieving a 62% overall success rate. These results suggest that pre-trained manipulation VLAs, with appropriate data augmentation and physics-informed guidance, can transfer to aerial manipulation and navigation, as well as the composition of these tasks.