mlsanityData to 14 Oct 2026

Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning

arXiv 2407.00889 ↗First posted 1 Jul 2024cs.ROcited by 0

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

Abstract (arXiv)

With the continual adoption of Uncrewed Aerial Vehicles (UAVs) across a wide-variety of application spaces, robust aerial manipulation remains a key research challenge. Aerial manipulation tasks require interacting with objects in the environment, often without knowing their dynamical properties like mass and friction a priori. Additionally, interacting with these objects can have a significant impact on the control and stability of the vehicle. We investigated an approach for robust control and non-prehensile aerial manipulation in unknown environments. In particular, we use model-based Deep Reinforcement Learning (DRL) to learn a world model of the environment while simultaneously learning a policy for interaction with the environment. We evaluated our approach on a series of push tasks by moving an object between goal locations and demonstrated repeatable behaviors across a range of friction values.

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 15 of its 25 references that are in the map. First posted on arXiv 1 Jul 2024.

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15 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding6 Feb

Other references · 14

  1. 2023Mastering Diverse Domains through World Modelscited by 1,253
  2. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  3. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  4. 2022DayDreamer: World Models for Physical Robot Learningcited by 400
  5. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 128
  6. 2021Learning High-Speed Flight in the Wildcited by 128
  7. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  8. 2021Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulationcited by 35
  9. 2022Learning Minimum-Time Flight in Cluttered Environmentscited by 16
  10. 2023Aerial Gym -- Isaac Gym Simulator for Aerial Robotscited by 11
  11. 2017A probabilistic data-driven model for planar pushingcited by 9
  12. 2023Survey of Simulators for Aerial Robots: An Overview and In-Depth Systematic Comparisonscited by 6
2 more
  1. 2020Self-Adapting Recurrent Models for Object Pushing from Learning in Simulationcited by 3
  2. 2023A Small Form Factor Aerial Research Vehicle for Pick-and-Place Tasks with Onboard Real-Time Object Detection and Visual Odometrycited by 1

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Close in wording, no citation either way

  1. 2026AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations
  2. 2024Dual Agent Learning Based Aerial Trajectory Tracking
  3. 2026From Local Whole-Body VLA Behaviors to Scene-Scale Aerial Manipulation
  4. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principles
  5. 2026AIR-VLA: Vision-Language-Action Systems for Aerial Manipulation
  6. 2025Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning
  7. 2025ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks
  8. 2026π, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation