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

Abstract-- Existing approaches for transporting and manipulating cable-suspended loads using multiple UA Vs 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 UA V 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. Unmanned aerial vehicles (UA Vs) have gained significant traction across domains such as surveillance, agriculture, and infrastructure inspection due to their agility and versatility. However, their limited payload capacity restricts their effectiveness in applications involving the transportation of heavy or bulky objects which is common in construction and large-scale logistics. A scalable and cost-effective solution to this limitation is cable-suspended cooperative aerial manipulation [1], where multiple UA Vs cooperatively transport and control a cable-suspended payload. This method enables full pose manipulation of objects whose weight may exceed the capacity of a single UA V . Numerous control strategies have been proposed for cooperative transportation of suspended payloads using UA V teams. These approaches vary in terms of modeling accuracy, scalability, communication requirements, and capability to regulate the full pose of the payload. Given the focus of this work on decentralized cooperative aerial manipulation, prior methods are categorized into three primary frameworks: centralized control, decentralized control with communication, and decentralized control without communication. Figure 1: We enable decentralized cooperative aerial manipulation through student policies that operate independently using only the ego UA V's state and the pose of the load. These student policies are trained via imitation learning from a centralized teacher policy with privileged observations, including the full state of the other UA Vs and the load. The policy has been tested in real-world environments, where three UA Vs cooperatively manipulate a cable-suspended load.