Robot Learning

Learning Rate

The learning rate is the scalar hyperparameter that controls the step size of gradient-based optimization, determining how much model parameters change per update. Values that are too large cause divergence or oscillation, while values too small slow convergence or trap training in poor regions. Practical training combines warmup phases, decay schedules such as cosine annealing, and adaptive optimizers like Adam that rescale steps per parameter.

Why it matters for physical AI

Fine-tuning pretrained robot foundation models is acutely learning-rate sensitive: overly aggressive updates destroy pretrained visual representations, while conservative ones fail to adapt the policy to new embodiments and tasks.