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October 19, 2025Open Access

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

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Non-differentiable reward optimization leads to better motion planning in autonomous robots, improving their safety.
The proposed reward-weighted dynamic thresholding algorithm enhances training on complex objectives, outperforming traditional methods.
Diffusion models capture agent interactions effectively, demonstrating their applicability in real-world dynamic environments.
State-of-the-art performance achieved on pedestrian datasets underscores the significance of this approach for autonomous systems.

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Reinforcement learning enhances motion planning by optimizing safety and effectiveness, suggesting improved performance.

Lee et al. (2025) studied this question.

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