Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

TL;DR

This paper introduces Co-jump, a multi-agent reinforcement learning framework enabling two quadrupedal robots to perform synchronized jumps beyond their individual limits without explicit communication, demonstrating significant improvements in jump height and coordination.

Contribution

The paper presents a novel decentralized multi-agent RL approach with curriculum learning for cooperative jumping, achieving high-performance synchronization without communication or pre-defined motions.

Findings

Robots successfully jump onto 1.5 m platforms in simulation and hardware.

Achieved 144% higher jump height than single robot.

Coordination achieved solely through proprioceptive feedback.

Abstract

While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities. We tackle the high-impulse contact dynamics of this task under a decentralized setting, achieving synchronization without explicit communication or pre-specified motion primitives. Our framework leverages Multi-Agent Proximal Policy Optimization (MAPPO) enhanced by a progressive curriculum strategy, which effectively overcomes the sparse-reward exploration challenges inherent in mechanically coupled systems. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height. Specifically, one of the robots achieves a foot-end elevation of 1.1 m, which represents a 144% improvement over the 0.45 m jump height of a standalone quadrupedal robot, demonstrating superior vertical performance. Notably, this precise coordination is achieved solely through proprioceptive feedback, establishing a foundation for communication-free collaborative locomotion in constrained environments.