Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans (2310.19427v1)

Abstract: Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks by training trajectory diffusion models and conditioning the sampled trajectories using auxiliary guidance functions. However, due to their nature as generative models, diffusion models are not guaranteed to generate feasible plans, resulting in failed execution and precluding planners from being useful in safety-critical applications. In this work, we propose a novel approach to refine unreliable plans generated by diffusion models by providing refining guidance to error-prone plans. To this end, we suggest a new metric named restoration gap for evaluating the quality of individual plans generated by the diffusion model. A restoration gap is estimated by a gap predictor which produces restoration gap guidance to refine a diffusion planner. We additionally present an attribution map regularizer to prevent adversarial refining guidance that could be generated from the sub-optimal gap predictor, which enables further refinement of infeasible plans. We demonstrate the effectiveness of our approach on three different benchmarks in offline control settings that require long-horizon planning. We also illustrate that our approach presents explainability by presenting the attribution maps of the gap predictor and highlighting error-prone transitions, allowing for a deeper understanding of the generated plans.

• The paper introduces the restoration gap metric to evaluate trajectory quality under noise-induced perturbations in diffusion-based planning.
• The paper utilizes Restoration Gap Guidance (RGG) to actively refine generated plans, ensuring enhanced feasibility and safety.
• Experimental results on Maze2D, Locomotion, and Block Stacking tasks demonstrate that the proposed framework outperforms existing methods.

Refining Diffusion Planner for Reliable Behavior Synthesis

The paper "Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans" addresses the inherent challenges in diffusion-based planning methods used in decision-making tasks with long horizons and sparse rewards. This research introduces a novel refinement approach for diffusion planners, focusing on detecting and correcting infeasible plans generated by diffusion models.

Key Contributions and Methods

The primary contribution of this work is the introduction of the "restoration gap," a novel metric designed to evaluate the quality of trajectories generated by diffusion models. The restoration gap is estimated through a gap predictor that identifies the extent to which a perturbed trajectory can be restored to its original form. This is achieved by modeling the plan under a noise-induced perturbation and evaluating its restoration capability using the diffusion model. The paper posits that feasible plans can closely return to their initial states even under noise, whereas infeasible plans exhibit significant deviations.

Further, the authors extend their approach by introducing the Restoration Gap Guidance (RGG) method. This approach leverages the gap predictor's output to adjust the generative process of diffusion planners actively. By minimizing the restoration gap, the RGG improves the feasibility of plans generated by diffusion models.

Additionally, the paper proposes an attribution map regularizer as a mechanism to mitigate adversarial guidance that could arise from sub-optimal estimations by the gap predictor. This regularizer employs attribution maps to identify influential transitions contributing to high restoration gaps, thus enhancing the reliability of refinement processes.

Experimental Results

The research showcases the effectiveness of the proposed methods across various benchmarks in offline control settings, including Maze2D, Locomotion tasks, and Block Stacking tasks. Results demonstrate that both the RGG and its regularized variant RGG+ outperform existing methods, notably improving planning performance in complex environments. The restoration gap effectively correlates with plan quality, as indicated by its alignment with performance improvements when plans are selected based on low restoration gaps.

Theoretical Implications

From a theoretical standpoint, the proposed restoration gap provides a bounded error probability for detecting infeasible plans under certain conditions, as proven by the authors. This theoretical foundation strengthens the claims of improved plan reliability and safety in applications involving safety-critical systems.

Practical Implications and Future Directions

Practically, this work highlights the potential of using generative model refinement techniques in real-world decision-making tasks, particularly where model failures could lead to significant adversarial consequences. The restoration gap allows practitioners to utilize diffusion models in a more controlled and reliable manner, ensuring the synthesized behaviors align with desired safety constraints.

Future research could extend the application of the restoration gap to more complex and high-dimensional control tasks, investigating the scalability and adaptability of the approach in heterogeneous and dynamic environments. Additionally, there might be new opportunities to integrate restoration gap metrics with other generative frameworks, further enhancing the robustness of behavior synthesis in artificial intelligence models.

In conclusion, this paper presents a significant advancement in diffusion-based planning methods by introducing a practical refinement strategy that emphasizes the feasibility and reliability of generated plans, establishing a new standard for behavior synthesis in artificial intelligence.