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Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
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site name
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2026-03-04 03:41:37
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Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following Brian Yang , Huangyuan Su , Nikolaos Gkanatsios , Tsung-Wei Ke , Ayush Jain , Jeff Schneider , Katerina Fragkiadaki Carnegie Mellon University, Paper arXiv Code Diffusion-ES is a flexible test-time trajectory optimization method for arbitrary reward functions that combines generative trajectory models and sampling-based search. We show how it can be used for state-of-the-art closed-loop planning and language instruction following in nuPlan. Abstract We present Diffusion-ES , a method which combines gradient-free optimization with trajectory denoising to optimize black-box non-differentiable objectives while staying in the data manifold. Reward-gradient guided denoising has been rec...
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