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.
ArXivabs/2506.05064(2025), https://api.semanticscholar.org/CorpusID:279244338
2 Pith papers cite this work. Polarity classification is still indexing.
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ESPADA uses semantic segmentation from VLMs and LLMs plus DTW to downsample non-critical segments in demonstrations, delivering about 2x faster robot execution in behavior cloning while maintaining task success rates.
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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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ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning
ESPADA uses semantic segmentation from VLMs and LLMs plus DTW to downsample non-critical segments in demonstrations, delivering about 2x faster robot execution in behavior cloning while maintaining task success rates.