Diffusion Model-Augmented Behavioral Cloning (2302.13335v4)

Abstract: 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.

• The paper’s main contribution is integrating diffusion models with behavioral cloning to jointly optimize conditional and joint probabilities for superior policy learning.
• The methodology employs a dual-objective loss that combines BC efficiency with the robust generalization of diffusion models trained on expert state-action pairs.
• Experimental results reveal that the proposed framework outperforms state-of-the-art baselines in continuous control tasks such as navigation and robotic manipulation.

Essay on Diffusion Model-Augmented Behavioral Cloning

The paper introduces an innovative imitation learning framework that leverages diffusion models to enhance Behavioral Cloning (BC), named Diffusion Model-Augmented Behavioral Cloning (DBC). This framework is designed to improve the generalization capabilities of policies derived from expert demonstrations by integrating the strengths of both conditional and joint probability modeling.

Introduction to Imitation Learning and Behavioral Cloning

Imitation learning facilitates the process of policy learning based on expert demonstrations in the absence of explicit reward signals. Among the various imitation learning strategies, Behavioral Cloning (BC) has been extensively employed due to its simplicity and stability. BC frames imitation as a supervised learning task where the agent learns the conditional probability $p(a|s)$ of actions conditioned on states by replicating expert actions. Despite its merits, BC struggles with generalization, particularly when encountering states not present in the training data.

Diffusion Models in Joint Probability Estimation

To mitigate the generalization issue inherent in BC, this work proposes leveraging diffusion models to complement the conditional probability framework with joint probability modeling. Diffusion models, a recent advancement in generative modeling, are employed to represent the joint distribution $p(s, a)$ of expert state-action pairs. The core idea is to enhance learning by integrating the strengths of both approaches—efficient action prediction through conditional probability and robust generalization via joint probability.

Framework and Methodology

The framework combines BC's efficiency with the generalization power of diffusion models by introducing a novel learning objective. This involves optimizing both the BC loss and a diffusion model loss to simultaneously capture the conditional and joint probabilities. The diffusion model is trained to model expert behaviors as a form of generative modeling, which in turn assists policy learning by providing a gradient-based estimate of how well a predicted action aligns with the expert distribution.

Experimental Validation

The framework's efficacy is validated across various continuous control tasks encompassing navigation, robotic arm manipulation, dexterous manipulation, and locomotion. The empirical results demonstrate that DBC generally outperforms or performs competitively against state-of-the-art baselines such as Implicit BC and Diffusion Policy, indicating its robustness and improved generalization capabilities. Notably, the framework shows robustness to dataset size variations, which further highlights its practical applicability.

Analysis and Future Directions

An in-depth analysis of the framework reveals the benefits of integrating conditional and joint probability models, particularly in achieving better generalization and efficiency. The diffusion model's denoising diffusion probabilistic process effectively captures and preserves the trajectory distributions of expert demonstrations. However, the framework introduces an additional hyperparameter for balancing the dual objectives, which might necessitate careful tuning for optimal performance across varied tasks.

Moving forward, the paper suggests potential extensions such as incorporating reinforcement learning-like interactions to further hone policies derived from DBC. As the framework is primarily designed for offline learning, extending this to accommodate online scenario interactions represents an intriguing avenue for future research.

In conclusion, Diffusion Model-Augmented Behavioral Cloning presents a compelling advancement in imitation learning by adeptly combining aspects of BC with diffusion model-based joint probability estimation. This integration not only enhances generalization but also provides a scalable approach to learning from complex demonstrations. The framework's contributions mark a significant step towards more autonomous and adaptable learning systems in fields such as robotics and autonomous navigation.