CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning

arXiv:2610.18425 · cs.RO, cs.MA · Submitted 2026-10-13 · Read on arXiv

cs.RO, cs.MA

Submitted: 2026-10-13

Updated: 2026-10-13

Comments: 8 pages

Code: https://github.com/Aerial-Manipulation-Lab/MARL_cooperative_aerial_manipulation_ext

Project page: https://petreric.github.io/CALM

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty
• Proximal Policy Optimization Algorithms

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• ARC: A Reasoning Recipe for Robot Foundation Models
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies