Motion Planning in Compressed Representation Spaces

2026-06-29

AI Summary

This work addresses the challenge of integrating data-driven deep learning with model-based planning to enable flexible, efficient, and realistic robot motion planning. It proposes a generative framework that, for the first time, compresses high-dimensional continuous trajectories into a hierarchical discrete latent space using a high-ratio autoencoder, and directly optimizes arbitrary task-specific objective functions in this latent space at test time to search for feasible motion plans. The approach requires no task-specific training and naturally supports multi-agent scenario synthesis and closed-loop planning. Experiments on the nuPlan and Waymo Open Motion Dataset demonstrate state-of-the-art performance in both closed-loop motion planning and multi-agent guided generation tasks.

Abstract

Deep learning methods have vastly expanded the capabilities of motion planning in robotics applications, as learning priors from large-scale data has been shown to be essential in capturing the highly complex behavior required for solving tasks such as manipulation or navigation for autonomous vehicles. At the same time, model-based planning algorithms based on search or optimization remain an essential tool due to their flexibility, efficiency, and the ability to incorporate domain knowledge via expert-designed algorithms and objective functions. We propose a new generative framework to unify these two paradigms. First, we learn an autoencoder with a high compression ratio and a latent space of hierarchically ordered, discrete-valued tokens. Leveraging both the dimensionality reduction and the hierarchical coarse-to-fine structure learned by this autoencoder, we then perform motion planning by directly searching in the latent space of tokens. This search can optimize arbitrary objective functions specified at test time, providing a large degree of flexibility while maintaining efficiency and producing realistic solutions by relying on the generative capabilities of the highly compressed autoencoder. We evaluate our method on nuPlan and the Waymo Open Motion Dataset, showing how latent space search can be used for a variety of guided behavior generation tasks, achieving strong performance for closed-loop motion planning and multi-agent guided scenario synthesis without requiring any task-specific training.

Problem

• motion planning
• compressed representation
• latent space
• autonomous vehicles
• behavior generation

Innovation

• compressed representation
• latent space planning
• hierarchical discrete tokens
• generative motion planning
• model-based search