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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 (Diffusion-BC) 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.

Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s, a). Despite the simplicity of modeling the conditional probability with BC, it usually struggles with generalization. While modeling the joint probability can improve generalization performance, the inference procedure is often time-consuming, and the model can suffer from manifold overfitting. This work proposes an imitation learning framework that benefits from modeling both the conditional and joint probability of the expert distribution. Our proposed Diffusion Model-Augmented Behavioral Cloning (DBC) employs a diffusion model trained to model expert behaviors and learns a policy to optimize both the BC loss (conditional) and our proposed diffusion model loss (joint). DBC outperforms baselines in various continuous control tasks in navigation, robot arm manipulation, dexterous manipulation, and locomotion. We design additional experiments to verify the limitations of modeling either the conditional probability or the joint probability of the expert distribution, as well as compare different generative models. Ablation studies justify the effectiveness of our design choices.

Hsiang-Chun Wang and Prof. Shao-Hua Sun presented their work “Diffusion Model-Augmented Behavioral Cloning” at the International Conference on Machine Learning (ICML) 2024 in Vienna, Austria in July 2024.