Robot Learning

End-to-End Learning

End-to-End Learning is the training of a single differentiable model that maps raw sensory input directly to control output, replacing hand-engineered pipelines of separate perception, state estimation, planning, and control modules. Levine et al.'s 2016 work on end-to-end visuomotor policies demonstrated pixels-to-torques manipulation, and the paradigm now dominates imitation-trained manipulation and vision-language-action models. Critiques center on interpretability, verification difficulty, and data hunger.

Why it matters for physical AI

Whether robot autonomy should be one large trained network or a structured stack with learned components is the field's central architectural debate, with end-to-end approaches currently propelled by foundation model scaling results.