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/2412.06782(2024),https://api.semanticscholar.org/CorpusID: 274610389
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
unclear 1representative citing papers
ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
Causal Spectral Policy decomposes actions spectrally into coarse motion from obs/language and conditional fine corrections, outperforming baselines on precision manipulation tasks.
citing papers explorer
-
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.
-
Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
-
Hierarchical Policy Learning via Spectral Decomposition
Causal Spectral Policy decomposes actions spectrally into coarse motion from obs/language and conditional fine corrections, outperforming baselines on precision manipulation tasks.