Hierarchical Policy Learning via Spectral Decomposition

• URL: http://arxiv.org/abs/2606.29570v1
• Date: Sun, 28 Jun 2026 19:22:40 GMT
• Title: Hierarchical Policy Learning via Spectral Decomposition
• Abstract summary: We identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements.<n>Motivated by this structure, we propose Causal Spectral Policy (CSP), which models action generation as a causal coarse-to-fine process.<n>CSP consistently outperforms strong baselines on precision-sensitive manipulation tasks.
• Score: 58.65543693788772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By analyzing actions in the spectral domain using the discrete cosine transform (DCT), we observe that low-frequency components capture global motion trajectories, while high-frequency components encode precise timing, alignment, and contact behaviors. Motivated by this structure, we propose Causal Spectral Policy (CSP), which models action generation as a causal coarse-to-fine process: coarse motion is predicted from observation and language, and fine corrections are generated conditionally on the realized trajectory. Across simulation and real-world evaluations, CSP consistently outperforms strong baselines on precision-sensitive manipulation tasks. Additionally, we propose human-inspired teleoperation noise injection as a data augmentation method, under which our approach demonstrates strong robustness to noisy demonstrations.

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