Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
Humans exhibit remarkable motor agility, which highlights the great potential of humanoid robots for athletic locomotion. Long rope skipping requires two rope turners to cooperatively swing the rope while adapting to a player under different jumping rhythms. We propose Marope, a multi-agent reinforcement learning (MARL) framework for cooperative long rope skipping with multiple humanoid robots. It adopts a hierarchical RL framework: the lower level learns decentralized rope manipulation policies through MARL, while the upper level trains a centralized scheduling policy to coordinate execution. Diverse jumping policies are incorporated to improve generalization across player behavioral styles. Experiments on Unitree G1 robots in simulation and the real world show that Marope outperforms baselines.