transfer learning

Researchers have unveiled ZEST, a framework allowing robots to transfer learned skills to new athletic tasks without additional training. The approach targets zero-shot embodied control, meaning a robot could apply movements from one physical activity directly to another it has never practiced. The work was highlighted in Science's news section, drawing attention from robotics researchers and observers of advances in physical machine learning.