Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning

TL;DR

This paper introduces a reinforcement learning approach to train diffusion models for autonomous motion planning, enabling explicit optimization of safety and effectiveness objectives that are non-differentiable, resulting in improved performance on pedestrian datasets.

Contribution

It proposes a reward-weighted dynamic thresholding algorithm for training diffusion models with non-differentiable objectives in motion planning.

Findings

Outperforms baselines on pedestrian datasets

Effectively optimizes safety and goal-reaching objectives

Demonstrates versatility in dynamic environments

Abstract

Safe and effective motion planning is crucial for autonomous robots. Diffusion models excel at capturing complex agent interactions, a fundamental aspect of decision-making in dynamic environments. Recent studies have successfully applied diffusion models to motion planning, demonstrating their competence in handling complex scenarios and accurately predicting multi-modal future trajectories. Despite their effectiveness, diffusion models have limitations in training objectives, as they approximate data distributions rather than explicitly capturing the underlying decision-making dynamics. However, the crux of motion planning lies in non-differentiable downstream objectives, such as safety (collision avoidance) and effectiveness (goal-reaching), which conventional learning algorithms cannot directly optimize. In this paper, we propose a reinforcement learning-based training scheme for diffusion motion planning models, enabling them to effectively learn non-differentiable objectives that explicitly measure safety and effectiveness. Specifically, we introduce a reward-weighted dynamic thresholding algorithm to shape a dense reward signal, facilitating more effective training and outperforming models trained with differentiable objectives. State-of-the-art performance on pedestrian datasets (CrowdNav, ETH-UCY) compared to various baselines demonstrates the versatility of our approach for safe and effective motion planning.