AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators

2025-12-11

AI Summary

Aerial manipulators operating at high speeds suffer from strong nonlinear, nonstationary inertial and aerodynamic coupling effects, rendering conventional analytical models insufficiently accurate; standard data-driven approaches fail to capture the heterogeneous distribution of residual dynamics across diverse operational conditions. Method: We propose a condition-aware lightweight temporal diffusion model—the first to integrate conditional diffusion mechanisms with temporal encoders—explicitly modeling the full probability distribution of residual forces, enabling robust prediction under abrupt condition shifts and unknown payloads. An adaptive controller is further incorporated for real-time compensation of dynamic uncertainties. Contribution/Results: Experiments demonstrate significant improvement in trajectory tracking accuracy. The method establishes a novel, interpretable, and generalizable paradigm for residual dynamics modeling in highly dynamic aerial manipulation, advancing both predictive fidelity and operational robustness.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Diffusion Models for Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces, making accurate dynamics modeling a core challenge for reliable control. Analytical models lose fidelity under these nonlinear and nonstationary effects, while standard data-driven methods such as deep neural networks and Gaussian processes cannot represent the diverse residual behaviors that arise across different operating conditions. We propose a regime-conditioned diffusion framework that models the full distribution of residual forces using a conditional diffusion process and a lightweight temporal encoder. The encoder extracts a compact summary of recent motion and configuration, enabling consistent residual predictions even through abrupt transitions or unseen payloads. When combined with an adaptive controller, the framework enables dynamics uncertainty compensation and yields markedly improved tracking accuracy in real-world tests.

Problem

• Models nonlinear dynamics in aerial manipulators
• Compensates for diverse residual forces across regimes
• Enables adaptive control with improved tracking accuracy

Innovation

• Regime-conditioned diffusion models residual force distribution
• Lightweight encoder extracts motion and configuration summaries
• Adaptive controller compensates dynamics uncertainty for tracking