Enabling Stateful Behaviors for Diffusion-based Policy Learning (2404.12539v3)

Abstract: While imitation learning provides a simple and effective framework for policy learning, acquiring consistent actions during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation at data curation stage or altering the model itself, both of which do not fully address the scalability of consistent action generation. To overcome this limitation, we introduce the Diff-Control policy, which utilizes a diffusion-based model to learn the action representation from a state-space modeling viewpoint. We demonstrate that we can reduce diffusion-based policies' uncertainty by making it stateful through a Bayesian formulation facilitated by ControlNet, leading to improved robustness and success rates. Our experimental results demonstrate the significance of incorporating action statefulness in policy learning, where Diff-Control shows improved performance across various tasks. Specifically, Diff-Control achieves an average success rate of 72% and 84% on stateful and dynamic tasks, respectively. Project page: https://github.com/ir-lab/Diff-Control

• The paper introduces Diff-Control, a novel diffusion-based policy that employs Bayesian filters to generate stateful actions.
• It leverages ControlNet and deep state-space models to integrate temporal dynamics for improved action consistency.
• Experimental results show a 72% success rate in stateful tasks and 84% in dynamic tasks, demonstrating robust performance.

Enabling Stateful Behaviors for Diffusion-based Policy Learning

The paper "Enabling Stateful Behaviors for Diffusion-based Policy Learning" by Xiao Liu, Fabian Weigend, Yifan Zhou, and Heni Ben Amor addresses a significant challenge in the domain of policy learning—achieving consistent actions in robotic execution under imitation learning frameworks. Traditional approaches have largely focused on modifying action representations during data collection or altering the model architecture, often failing to fully address the scalability issues related to consistent action generation. The authors propose an innovative approach using a diffusion-based model to incorporate stateful actions, enhancing both the robustness and effectiveness of learned policies through a Bayesian formulation.

Overview of the Approach

The core contribution of this paper is the introduction of Diff-Control, a policy that leverages a diffusion-based framework to capture action statefulness. The method utilizes ControlNet as a transition model, embedding Bayesian filters into the policy learning process to facilitate consistent action generation. This contrasts with previous approaches that predominantly relied on static action representations.

Diffusion models are typically used to address multimodal distributions, making them suitable for modeling the diverse range of robot actions. By adopting a Bayesian perspective, Diff-Control ensures that the generated actions remain consistent over time by explicitly integrating temporal dynamics within the action space. This is achieved through the structure of deep state-space models (DSSMs), which allow the identification of dynamic patterns necessary for robust policy execution.

Experimental Results

The experimental evaluation highlights the practical merits of the Diff-Control policy across various tasks, achieving significant improvements in success rates. Specifically, the model achieved an average success rate of 72% in stateful tasks and 84% in dynamic tasks, indicating its capability to handle both temporal consistency and adaptability in varying contexts. These results underscore the practical utility of incorporating state tracking in policy learning algorithms.

Key Advances

The paper outlines several important contributions:

• The integration of a recursive Bayesian filter within diffusion-based policies is a novel approach, ensuring action consistency by utilizing the ControlNet structure as a transition model.
• The demonstration of enhanced success rates in dynamic and temporal tasks, with improvements reaching up to 48% as compared to existing state-of-the-art methods. This performance boost is attributed to Diff-Control’s ability to accurately track and predict state transitions.
• The robustness of Diff-Control against perturbations, maintaining a high success rate with a minimum of 30% improvement over baseline approaches. This resilience is particularly critical for real-world applications where environmental conditions are subject to change.

Implications and Future Directions

The implications of integrating stateful behaviors into policy learning are substantial. The approach can pave the way for more adaptive and reliable robotic behaviors, which are crucial for applications requiring high precision and consistency. By effectively managing action variability and ensuring temporal coherence, Diff-Control enhances the deployment potential of robots in complex environments.

Future research could explore further enhancements in diffusion-based models by integrating additional sensory modalities or extending the Bayesian framework to incorporate more complex probabilistic reasoning. Additionally, the approach could be validated across a broader spectrum of robotic tasks and settings, further solidifying its applicability in the domain of autonomous systems.

In conclusion, this paper provides a compelling methodology for enhancing policy learning frameworks, contributing both theoretical insights and practical advancements towards stateful policy implementations in robotics.