Cognizing and Imitating Robotic Skills via a Dual Cognition-Action Architecture

Abstract

Enabling robots to effectively learn and imitate expert skills in long-horizon tasks remains challenging. Hierarchical imitation learning (HIL) approaches have made strides but often fall short in complex scenarios due to their reliance on self-exploration. This paper introduces a novel approach inspired by the human skill acquisition process, proposing a Cognition-Action-based Robotic Skill Imitation Learning (CasIL) framework. CasIL integrates human cognitive priors for task decomposition into a dual-layer architecture, enhancing robots' ability to cognize and imitate essential skills from expert demonstrations. Our experiments across four RLbench tasks demonstrate CasIL's superior performance, robustness, and generalizability in skill imitation compared to related methods.