Decentralized Aerial Manipulation of a Cable-Suspended Load using
  Multi-Agent Reinforcement Learning
This paper presents the first decentralized method to enable real-world 6-DoF
manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles
(MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train
an outer-loop control policy for each MAV. Unlike state-of-the-art controllers
that utilize a centralized scheme, our policy does not require global states,
inter-MAV communications, nor neighboring MAV information. Instead, agents
communicate implicitly through load pose observations alone, which enables high
scalability and flexibility. It also significantly reduces computing costs
during inference time, enabling onboard deployment of the policy. In addition,
we introduce a new action space design for the MAVs using linear acceleration
and body rates. This choice, combined with a robust low-level controller,
enables reliable sim-to-real transfer despite significant uncertainties caused
by cable tension during dynamic 3D motion. We validate our method in various
real-world experiments, including full-pose control under load model
uncertainties, showing setpoint tracking performance comparable to the
state-of-the-art centralized method. We also demonstrate cooperation amongst
agents with heterogeneous control policies, and robustness to the complete
in-flight loss of one MAV. Videos of experiments:
https://autonomousrobots.nl/paper_websites/aerial-manipulation-marl
689ddcb9444103934017ef5c
Jack Zeng
claimed_verified
2025-08-14T13:38:16.141Z
6895e27da51af9aacdf40bfc
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/o6LhpvE5XuHQJBZPvZDLW.jpeg
Jack Zeng
user
jackzeng-robotics
689ddcb9444103934017ef5d
Andreu Matoses Gimenez
689ddcb9444103934017ef5e
Eugene Vinitsky
689ddcb9444103934017ef5f
Javier Alonso-Mora
689ddcb9444103934017ef60
Sihao Sun
claimed_verified
2025-08-18T06:58:50.149Z
689df0782d22b861554e11f4
https://cdn-avatars.huggingface.co/v1/production/uploads/689df0782d22b861554e11f4/yObfniNXtQhbpb6Rr9bQP.jpeg
Sun
user
SihaoSun
Decentralized Aerial Manipulation of a Cable-Suspended Load using
  Multi-Agent Reinforcement Learning
This paper presents the first decentralized method to enable real-world 6-DoF
manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles
(MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train
an outer-loop control policy for each MAV. Unlike state-of-the-art controllers
that utilize a centralized scheme, our policy does not require global states,
inter-MAV communications, nor neighboring MAV information. Instead, agents
communicate implicitly through load pose observations alone, which enables high
scalability and flexibility. It also significantly reduces computing costs
during inference time, enabling onboard deployment of the policy. In addition,
we introduce a new action space design for the MAVs using linear acceleration
and body rates. This choice, combined with a robust low-level controller,
enables reliable sim-to-real transfer despite significant uncertainties caused
by cable tension during dynamic 3D motion. We validate our method in various
real-world experiments, including full-pose control under load model
uncertainties, showing setpoint tracking performance comparable to the
state-of-the-art centralized method. We also demonstrate cooperation amongst
agents with heterogeneous control policies, and robustness to the complete
in-flight loss of one MAV. Videos of experiments:
https://autonomousrobots.nl/paper_websites/aerial-manipulation-marl
A decentralized multi-agent reinforcement learning method enables real-world 6-DoF manipulation of cable-suspended loads using MAVs, achieving performance comparable to centralized methods with improved scalability and robustness.
multi-agent reinforcement learning MARL outer-loop control policy decentralized control load pose observations linear acceleration body rates low-level controller sim-to-real transfer setpoint tracking heterogeneous control policies