Planning with Diffusion for Flexible Behavior Synthesis

TL;DR

This paper introduces a diffusion probabilistic model for control that unifies planning and modeling, enabling flexible, long-horizon decision-making by iteratively denoising trajectories, and demonstrating advantages over traditional model-based RL methods.

Contribution

It proposes a novel diffusion-based planning framework that integrates trajectory optimization into the modeling process, enhancing flexibility and performance in control tasks.

Findings

Effective long-horizon decision-making demonstrated

Diffusion-based planning outperforms classical methods in flexibility

Unifies sampling and planning processes

Abstract

Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization. In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories. We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility.