CrazyMARL: Decentralized Direct Motor Control Policies for Cooperative Aerial Transport of Cable-Suspended Payloads

2025-09-17

AI Summary

Coordinated cable-suspended load transportation by multi-UAV systems faces significant challenges, including external disturbances, highly nonlinear load dynamics, and frequent transitions between slack and taut rope modes. Method: This paper proposes a decentralized end-to-end reinforcement learning control framework that explicitly models and handles rope-mode switching—departing from conventional rigid-link assumptions—and directly outputs motor-level control commands. It incorporates a simulation-to-real zero-shot transfer strategy to bridge the reality gap. Contribution/Results: In simulation under extreme conditions, the method achieves an 80% recovery rate—substantially outperforming baseline approaches (44%). Crucially, it demonstrates zero-shot deployment on real hardware without fine-tuning, validating exceptional robustness and generalization capability across dynamic rope-mode transitions and environmental uncertainties.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Collaborative transportation of cable-suspended payloads by teams of Unmanned Aerial Vehicles (UAVs) has the potential to enhance payload capacity, adapt to different payload shapes, and provide built-in compliance, making it attractive for applications ranging from disaster relief to precision logistics. However, multi-UAV coordination under disturbances, nonlinear payload dynamics, and slack--taut cable modes remains a challenging control problem. To our knowledge, no prior work has addressed these cable mode transitions in the multi-UAV context, instead relying on simplifying rigid-link assumptions. We propose CrazyMARL, a decentralized Reinforcement Learning (RL) framework for multi-UAV cable-suspended payload transport. Simulation results demonstrate that the learned policies can outperform classical decentralized controllers in terms of disturbance rejection and tracking precision, achieving an 80% recovery rate from harsh conditions compared to 44% for the baseline method. We also achieve successful zero-shot sim-to-real transfer and demonstrate that our policies are highly robust under harsh conditions, including wind, random external disturbances, and transitions between slack and taut cable dynamics. This work paves the way for autonomous, resilient UAV teams capable of executing complex payload missions in unstructured environments.

Problem

• Decentralized control for multi-UAV cable-suspended payload transport
• Handling cable mode transitions under disturbances and dynamics
• Achieving robust performance in unstructured environments with disturbances

Innovation

• Decentralized Reinforcement Learning framework
• Direct motor control policies
• Handles slack-taut cable transitions