Isaac Peterson

Heterogeneous Multi-Agent Learning in Isaac Lab: Scalable Simulation for Robotic Collaboration

Multi-Agent Reinforcement Learning (MARL) plays a crucial role in robotic coordination and control, yet existing simulation environments often lack the fidelity and scalability needed for real-world applications. In this work, we extend Isaac Lab to support efficient training of both homogeneous and