Article

Learning Cross-Coupled and Regime-Dependent Dynamics for Aerial Manipulation

A study proposes a structured encoder-decoder framework in which a nonlinear history encoder captures the temporal and cross-coupled residual dynamics of an aerial manipulation platform and its robotic arm, while a linear latent-space decoder supports rapid adaptation at deployment time. The framework's linear-in-parameters structure enables closed-form Bayesian updates and integration with real-time model predictive control (MPC). Experiments on a real aerial manipulation platform showed improved residual prediction accuracy, rapid adaptation after dynamics changes, and reduced MPC tracking error under changing payload conditions.

Part of the event: Proposing a Cross-Coupled and Operating-Condition-Dependent Dynamics Learning Framework for Aerial Manipulation

This page provides a summary and source link only.