Generalization in Robotic Manipulation

The field studies whether robots can manipulate objects reliably when their shape, position, appearance, surroundings, or physical properties differ from training, and whether skills transfer across tasks and robot bodies. Common approaches use object-centric geometry and affordances, reusable skills and action primitives, symmetry-aware policies, and vision-language-action or world models that connect instructions and predicted outcomes to control. Researchers also expand training through simulation, human videos, data augmentation, and diverse demonstrations, then test policies in settings such as LIBERO, RLBench, and real-world object manipulation. A central challenge is that high benchmark success does not always mean a robot has learned physical or compositional generalization; evaluations must distinguish genuine transfer from memorized patterns and expose failures under combined shifts.