DSSP is a history-conditioned diffusion state space policy that uses SSMs to encode full observation streams with an auxiliary dynamics objective and hierarchical fusion, achieving SOTA results with reduced model size in robot manipulation.
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models, June 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 3years
2026 3representative citing papers
Variational Regularization imposes an adaptive information bottleneck on noisy intermediate features in DP3-UNet and DP3-DiT policies, consistently raising task success rates on RoboTwin2.0, Adroit, and MetaWorld while achieving new state-of-the-art results.
FocalPolicy introduces frequency-optimized chunking and locally anchored flow matching with a foresight composite objective to reduce inter-chunk discontinuities in visuomotor policies.
citing papers explorer
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DSSP: Diffusion State Space Policy with Full-History Encoding
DSSP is a history-conditioned diffusion state space policy that uses SSMs to encode full observation streams with an auxiliary dynamics objective and hierarchical fusion, achieving SOTA results with reduced model size in robot manipulation.
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Information Filtering via Variational Regularization for Robot Manipulation
Variational Regularization imposes an adaptive information bottleneck on noisy intermediate features in DP3-UNet and DP3-DiT policies, consistently raising task success rates on RoboTwin2.0, Adroit, and MetaWorld while achieving new state-of-the-art results.
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FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy
FocalPolicy introduces frequency-optimized chunking and locally anchored flow matching with a foresight composite objective to reduce inter-chunk discontinuities in visuomotor policies.