  Designing motion control and planning algorithms for multilift systems
remains challenging due to the complexities of dynamics, collision avoidance,
actuator limits, and scalability. Existing methods that use optimization and
distributed techniques effectively address these constraints and scalability
issues. However, they often require substantial manual tuning, leading to
suboptimal performance. This paper proposes Auto-Multilift, a novel framework
that automates the tuning of model predictive controllers (MPCs) for multilift
systems. We model the MPC cost functions with deep neural networks (DNNs),
enabling fast online adaptation to various scenarios. We develop a distributed
policy gradient algorithm to train these DNNs efficiently in a closed-loop
manner. Central to our algorithm is distributed sensitivity propagation, which
is built on fully exploiting the unique dynamic couplings within the multilift
system. It parallelizes gradient computation across quadrotors and focuses on
actual system state sensitivities relative to key MPC parameters. Extensive
simulations demonstrate favorable scalability to a large number of quadrotors.
Our method outperforms a state-of-the-art open-loop MPC tuning approach by
effectively learning adaptive MPCs from trajectory tracking errors. It also
excels in learning an adaptive reference for reconfiguring the system when
traversing multiple narrow slots.
