mlsanityData to 14 Oct 2026

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

arXiv 2506.13478 ↗First posted 16 Jun 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

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.

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.

Builds on

8 of 27 references.

References · 8

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2016Automatic LQR Tuning Based on Gaussian Process Global Optimizationcited by 30
  4. 2017Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllerscited by 3
  5. 2019Development of SAM: cable-Suspended Aerial Manipulatorcited by 3
  6. 2020Optimal Oscillation Damping Control of cable-Suspended Aerial Manipulator with a Single IMU Sensorcited by 1
  7. 2023Hierarchical Whole-body Control of the cable-Suspended Aerial Manipulator endowed with Winch-based Actuationcited by 1
  8. 2024Observer-based Controller Design for Oscillation Damping of a Novel Suspended Underactuated Aerial Platformcited by 1

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  1. 2026SwingRL: Adaptive Observation Reinforcement Learning with World-Model Prediction for Cable-Suspended Hoisting Control
  2. 2026Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty
  3. 2026AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations
  4. 2025Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning
  5. 2026CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning ↗
  6. 2025Learning Agile Tensile Perching for Aerial Robots from Demonstrations
  7. 2025Flying Hand: End-Effector-Centric Framework for Versatile Aerial Manipulation Teleoperation and Policy Learning
  8. 2025Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning