Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation

2026-05-14

AI Summary

This work addresses the challenge of inaccurate dynamics modeling for aerial manipulators in complex tasks, where strong coupling, aerodynamic delays, and abrupt changes in operating conditions degrade performance. To this end, the authors propose a structured encoder-decoder framework that employs a nonlinear latent-variable encoder to capture cross-variable couplings and temporal dependencies from state-input histories, paired with a lightweight linear parameterized decoder for efficient online adaptation. Innovatively integrating closed-form Bayesian online learning with a covariance inflation strategy, the approach achieves both rapid responsiveness and stability under nonstationary dynamics, while seamlessly interfacing with real-time model predictive control. Experimental results demonstrate that the method significantly improves residual prediction accuracy, enabling faster adaptation and superior trajectory tracking performance during sudden changes in operational conditions.

Abstract

Accurate dynamics models are critical for aerial manipulators operating under complex tasks such as payload transport. However, modeling these systems remains fundamentally challenging due to strong quadrotor-manipulator coupling, delayed aerodynamic interactions, and regime-dependent dynamics variations arising from payload changes and manipulator reconfiguration. These effects produce residual dynamics that are simultaneously cross-coupled, history-dependent, and nonstationary, causing both analytical models and purely offline learned models to degrade during deployment. To address these challenges, we propose a structured encoder-decoder framework for adaptive residual dynamics learning in aerial manipulators. The proposed nonlinear latent encoder captures cross-variable coupling and temporal dependencies from state-input histories, while a lightweight linear latent decoder enables online adaptation under regime-dependent nonstationary dynamics. The linear-in-parameter decoder structure permits closed-form Bayesian adaptation together with consistency-driven covariance inflation, enabling rapid and stable adaptation to both transient and slowly varying dynamics changes while remaining compatible with real-time model predictive control (MPC). Experimental results on a real aerial manipulation platform demonstrate improved residual prediction accuracy, faster adaptation under changing operating conditions, and enhanced MPC-based trajectory tracking performance. These results highlight the importance of jointly modeling coupled temporal dynamics and deployment-time nonstationarity for reliable aerial manipulation.

Problem

• aerial manipulation
• cross-coupled dynamics
• regime-dependent dynamics
• nonstationary dynamics
• residual dynamics

Innovation

• adaptive dynamics learning
• cross-coupled dynamics
• regime-dependent nonstationarity
• encoder-decoder framework
• Bayesian online adaptation