# Automatic Generation of Aerobatic Flight in Complex Environments via Diffusion Models

arXiv: https://arxiv.org/abs/2504.15138 · HTML: https://mlsanity.com/paper/2504.15138/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 25 of 25)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://mlsanity.com/paper/2308.01557/) (2023)
- [CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control](https://mlsanity.com/paper/2410.03441/) (2024)
- [Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models](https://mlsanity.com/paper/2401.03360/) (2023)
- [Generating Human Interaction Motions in Scenes with Text Control](https://mlsanity.com/paper/2404.10685/) (2024)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories](https://mlsanity.com/paper/1712.02402/) (2017)
- [Geometrically Constrained Trajectory Optimization for Multicopters](https://mlsanity.com/paper/2103.00190/) (2021)
- [RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System](https://mlsanity.com/paper/2209.08533/) (2022)
- [LongDanceDiff: Long-term Dance Generation with Conditional Diffusion Model](https://mlsanity.com/paper/2308.11945/) (2023)
- [Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios](https://mlsanity.com/paper/2312.11866/) (2023)
- [Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness](https://mlsanity.com/paper/2207.03524/) (2022)
- [Robust Trajectory Planning for Spatial-Temporal Multi-Drone Coordination in Large Scenes](https://mlsanity.com/paper/2109.08403/) (2021)
- [Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights](https://mlsanity.com/paper/2212.11552/) (2022)
- [Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space](https://mlsanity.com/paper/2403.02977/) (2024)

## Built on by (most cited first, 2 of 2)

- [FlightDiffusion: Revolutionising Autonomous Drone Training with Diffusion Models Generating FPV Video](https://mlsanity.com/paper/2509.14082/) (2025)
- [Safer Trajectory Planning with CBF-guided Diffusion Model for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2604.17527/) (2026)

## Abstract (arXiv)

Performing striking aerobatic flight in complex environments demands manual designs of key maneuvers in advance, which is intricate and time-consuming as the horizon of the trajectory performed becomes long. This paper presents a novel framework that leverages diffusion models to automate and scale up aerobatic trajectory generation. Our key innovation is the decomposition of complex maneuvers into aerobatic primitives, which are short frame sequences that act as building blocks, featuring critical aerobatic behaviors for tractable trajectory synthesis. The model learns aerobatic primitives using historical trajectory observations as dynamic priors to ensure motion continuity, with additional conditional inputs (target waypoints and optional action constraints) integrated to enable user-editable trajectory generation. During model inference, classifier guidance is incorporated with batch sampling to achieve obstacle avoidance. Additionally, the generated outcomes are refined through post-processing with spatial-temporal trajectory optimization to ensure dynamical feasibility. Extensive simulations and real-world experiments have validated the key component designs of our method, demonstrating its feasibility for deploying on real drones to achieve long-horizon aerobatic flight.