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-SSM based policy (DiSPo) that learns from diverse coarse skills and produces varying control scales of actions by leveraging a state-space model, 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.
View on arXiv@article{oh2025_2409.14719,
title={ DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization },
author={ Nayoung Oh and Jaehyeong Jang and Moonkyeong Jung and Daehyung Park },
journal={arXiv preprint arXiv:2409.14719},
year={ 2025 }
}