XIL Advancing Robot Imitation Learning
X-IL is a modular open-source framework for imitation learning. It allows flexible experimentation with modern techniques, integrating novel architectures and multi-modal learning. X-IL decomposes the IL process into observation representations, backbones, architectures, and policy representations, enabling easy swapping of components and achieving superior performance on robotic benchmarks, especially with xLSTM and multi-modal inputs.