Reproduce “Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control” on OpenResearch

About this paper

Tong Jian, Tianyu Dai, Tao Yu

Analog Devices researchers developed an in-context learning (ICL) framework that applies transformer models, pretrained on diverse synthetic data, to real-world motor feedforward control. This approach enables one-shot adaptation to predict torque currents for new motors and loads, consistently outperforming traditional PI and physics-based controllers by reducing Root Mean Squared Error across various challenging conditions.

Key insights

• In-context learning (ICL) can be effectively extended from abstract domains to generalize to complex, nonlinear real-world physical systems, demonstrated through its application in motor feedforward control.
• Pretraining on a diverse corpus of synthetic data is a powerful strategy to develop robust AI models that can achieve efficient, data-scarce adaptation to real-world physical systems.
• A two-stage training approach, separating generic signal representation learning from specific system behavior learning, enhances generalization and data efficiency for adaptive control tasks.

Summary by alphaXiv.