# Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework

arXiv: https://arxiv.org/abs/2506.13478 · HTML: https://mlsanity.com/paper/2506.13478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 8 of 8)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Automatic LQR Tuning Based on Gaussian Process Global Optimization](https://mlsanity.com/paper/1605.01950/) (2016)
- [Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers](https://mlsanity.com/paper/1703.02899/) (2017)
- [Development of SAM: cable-Suspended Aerial Manipulator](https://mlsanity.com/paper/1903.02426/) (2019)
- [Optimal Oscillation Damping Control of cable-Suspended Aerial Manipulator with a Single IMU Sensor](https://mlsanity.com/paper/2003.00472/) (2020)
- [Hierarchical Whole-body Control of the cable-Suspended Aerial Manipulator endowed with Winch-based Actuation](https://mlsanity.com/paper/2305.16466/) (2023)
- [Observer-based Controller Design for Oscillation Damping of a Novel Suspended Underactuated Aerial Platform](https://mlsanity.com/paper/2401.17676/) (2024)

## Abstract (arXiv)

In this work, we present a novel approach to augment a model-based control method with a reinforcement learning (RL) agent and demonstrate a swing-up maneuver with a suspended aerial manipulation platform. These platforms are targeted towards a wide range of applications on construction sites involving cranes, with swing-up maneuvers allowing it to perch at a given location, inaccessible with purely the thrust force of the platform. Our proposed approach is based on a hierarchical control framework, which allows different tasks to be executed according to their assigned priorities. An RL agent is then subsequently utilized to adjust the reference set-point of the lower-priority tasks to perform the swing-up maneuver, which is confined in the nullspace of the higher-priority tasks, such as maintaining a specific orientation and position of the end-effector. Our approach is validated using extensive numerical simulation studies.