HiPolicy is a new hierarchical multi-frequency action chunking method for imitation learning that jointly generates coarse and fine action sequences with entropy-guided execution to improve performance and efficiency in robotic manipulation.
Advances in neural information processing systems34, 11287–11302 (2021)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Structured state-space regularization induces spectral structure in image tokenizer latent spaces via an SSM-derived objective, improving generative performance with minimal reconstruction loss.
citing papers explorer
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HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
HiPolicy is a new hierarchical multi-frequency action chunking method for imitation learning that jointly generates coarse and fine action sequences with entropy-guided execution to improve performance and efficiency in robotic manipulation.
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Structured State-Space Regularization for Generation-Friendly Image Tokenization
Structured state-space regularization induces spectral structure in image tokenizer latent spaces via an SSM-derived objective, improving generative performance with minimal reconstruction loss.