タスク条件付き不確実性を考慮した事前分布による階層的拡散動作計画
Hierarchical Diffusion Motion Planning with Task-Conditioned Uncertainty-Aware Priors
タスクと動作の構造をノイズモデルに組み込んだ階層的拡散プランナを提案し、GPMPに基づく構造化ガウス事前分布で軌道生成の成功率と滑らかさを向上させた。
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著者: Amelie Minji Kim, Anqi Wu, Ye Zhao
分類: cs.RO
原文アブストラクト
We propose a novel hierarchical diffusion planner that embeds task and motion structure directly into the noise model. Unlike standard diffusion-based planners that rely on zero-mean, isotropic Gaussian corruption, we introduce task-conditioned structured Gaussians whose means and covariances are derived from Gaussian Process Motion Planning (GPMP), explicitly encoding trajectory smoothness and task semantics in the prior. We first generalize the standard diffusion process to biased, non-isotropic corruption with closed-form forward and posterior expressions. Building on this formulation, our hierarchical design separates prior instantiation from trajectory denoising. At the upper level, the model predicts sparse, task-centric key states and their associated timings, which instantiate a structured Gaussian prior (mean and covariance). At the lower level, the full trajectory is denoised under this fixed prior, treating the upper-level outputs as noisy observations. Experiments on Maze2D goal-reaching and KUKA block stacking show consistently higher success rates and smoother trajectories than isotropic baselines, achieving dataset-level smoothness substantially earlier during training. Ablation studies further show that explicitly structuring the corruption process provides benefits beyond neural conditioning the denoising network alone. Overall, our approach concentrates the prior's probability mass near feasible and semantically meaningful trajectories. Our project page is available at https://hta-diffusion.github.io.
