# DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization

arXiv: https://arxiv.org/abs/2409.14719 · HTML: https://mlsanity.com/paper/2409.14719/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba).
- First posted on arXiv 23 Sep 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Video, motion and generative Mamba models](https://mlsanity.com/ssm/video-motion-and-generative-mamba-models/)
- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 3 of 3)

- [AnoleVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation](https://mlsanity.com/paper/2603.15046/) (2026)
- [ParCo-SDF: Learning Prior-Free Partial-to-Complete Signed Distance Fields of Deformable Objects](https://mlsanity.com/paper/2605.29417/) (2026)
- [ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation](https://mlsanity.com/paper/2609.35200/) (2026)

## Abstract (arXiv)

We aim to solve the problem of generating coarse-to-fine skills learning from demonstrations (LfD). To scale precision, traditional LfD approaches often rely on extensive fine-grained demonstrations with external interpolations or dynamics models with limited generalization capabilities. For memory-efficient learning and convenient granularity change, we propose a novel diffusion-state space model (SSM) based policy (DiSPo) that learns from diverse coarse skills and produces varying control scales of actions by leveraging an SSM, Mamba. Our evaluations show the adoption of Mamba and the proposed step-scaling method enable DiSPo to outperform in three coarse-to-fine benchmark tests with maximum 81% higher success rate than baselines. In addition, DiSPo improves inference efficiency by generating coarse motions in less critical regions. We finally demonstrate the scalability of actions with simulation and real-world manipulation tasks. Code and Videos are available at https://robo-dispo.github.io.