Robot Learning

Transfer Learning

Transfer learning is the reuse of knowledge acquired on a source task, domain, or embodiment to improve learning on a related target, typically by fine-tuning pretrained representations rather than training from scratch. In robotics it spans transferring visual features from web-scale pretraining into control, adapting policies across tasks, sim-to-real transfer, and cross-embodiment transfer between different robots, as demonstrated at scale by the RT-X models on Open X-Embodiment.

Why it matters for physical AI

Robots cannot afford to learn every task from zero given the cost of physical data, so transfer from prior models, simulation, and other embodiments is the economic foundation of the entire robot foundation model paradigm.