# Decentralized Real-Time Planning for Multi-UAV Cooperative Manipulation via Imitation Learning

arXiv: https://arxiv.org/abs/2510.17143 · HTML: https://mlsanity.com/paper/2510.17143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 10 of 10)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics](https://mlsanity.com/paper/2406.14558/) (2024)
- [Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight](https://mlsanity.com/paper/2307.06100/) (2023)
- [A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight](https://mlsanity.com/paper/2109.01365/) (2021)
- [Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles](https://mlsanity.com/paper/1611.09240/) (2016)
- [Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load](https://mlsanity.com/paper/2501.18802/) (2025)
- [Nonlinear Model Predictive Control for Cooperative Transportation and Manipulation of Cable Suspended Payloads with Multiple Quadrotors](https://mlsanity.com/paper/2303.06165/) (2023)
- [Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information](https://mlsanity.com/paper/2107.09647/) (2021)
- [Flexible collaborative transportation by a team of rotorcraft](https://mlsanity.com/paper/1902.00279/) (2019)

## Built on by (most cited first, 1 of 1)

- [Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions](https://mlsanity.com/paper/2605.19562/) (2026)

## Abstract (arXiv)

Existing approaches for transporting and manipulating cable-suspended loads using multiple UAVs along reference trajectories typically rely on either centralized control architectures or reliable inter-agent communication. In this work, we propose a novel machine learning based method for decentralized kinodynamic planning that operates effectively under partial observability and without inter-agent communication. Our method leverages imitation learning to train a decentralized student policy for each UAV by imitating a centralized kinodynamic motion planner with access to privileged global observations. The student policy generates smooth trajectories using physics-informed neural networks that respect the derivative relationships in motion. During training, the student policies utilize the full trajectory generated by the teacher policy, leading to improved sample efficiency. Moreover, each student policy can be trained in under two hours on a standard laptop. We validate our method in both simulation and real-world environments to follow an agile reference trajectory, demonstrating performance comparable to that of centralized approaches.