Diffusion Model-Augmented Behavioral Cloning
This work aims to augment BC by employing diffusion models for modeling
expert behaviors, and designing a learning objective that leverages
learned diffusion models to guide policy learning. To this end, we
propose diffusion model-augmented behavioral cloning (DBC) that combines
our proposed diffusion model guided learning objective with the BC
objective, which complements each other. Our proposed method outperforms
baselines or achieves competitive performance in various continuous
control domains, including navigation, robot arm manipulation, and
locomotion.