MoMo is a two-stage imitation learning framework for robot manipulation. It uses a spatiotemporal action tokenizer and a behavior-cloning transformer to condition robot actions on continuous motion-mode inputs. The approach allows developers to parameterize execution-level variation across diverse tasks, improving behavioral adaptability in physical robotics.
Opening Kapyn…