mlsanityData to 14 Oct 2026

Reinforcement Learning Driven Cooperative Ball Balance in Rigidly Coupled Drones

arXiv 2404.19070 ↗First posted 29 Apr 2024cs.ROcited by 0

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

Abstract (arXiv)

Multi-drone cooperative transport (CT) problem has been widely studied in the literature. However, limited work exists on control of such systems in the presence of time-varying uncertainties, such as the time-varying center of gravity (CG). This paper presents a leader-follower approach for the control of a multi-drone CT system with time-varying CG. The leader uses a traditional Proportional-Integral-Derivative (PID) controller, and in contrast, the follower uses a deep reinforcement learning (RL) controller using only local information and minimal leader information. Extensive simulation results are presented, showing the effectiveness of the proposed method over a previously developed adaptive controller and for variations in the mass of the objects being transported and CG speeds. Preliminary experimental work also demonstrates ball balance (depicting moving CG) on a stick/rod lifted by two Crazyflie drones cooperatively.

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 5 of its 27 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

5 of 27 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  3. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  4. 2017Control of a Quadrotor with Reinforcement Learningcited by 92
  5. 2018Composable Deep Reinforcement Learning for Robotic Manipulationcited by 72

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  1. 2025Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
  2. 2026CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning ↗
  3. 2026Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
  4. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  5. 2024A Learning-based Quadcopter Controller with Extreme Adaptation
  6. 2025Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
  7. 2025JuggleRL: Mastering Ball Juggling with a Quadrotor via Deep Reinforcement Learning
  8. 2024Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning