DiSPo：基于扩散-状态空间模型的策略学习用于粗到细动作离散化
DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization
AI总结：
DiSPo通过结合扩散-状态空间模型和步骤缩放方法，实现了高效且灵活的粗到细动作离散化学习，显著提升了成功率和推理效率。
摘要：
我们旨在解决从演示中生成粗到细技能学习（LfD）的问题。为了提高精度，传统LfD方法通常依赖于大量的细粒度演示或外部插值或动态模型，这些模型的泛化能力有限。为实现内存高效的学和方便的粒度变化，我们提出了一种新的基于扩散-状态空间模型（SSM）的策略（DiSPo），该策略通过利用SSM Mamba来学习多种粗技能，并生成不同控制尺度的动作。我们的评估显示，采用Mamba和所提出的步骤缩放方法使DiSPo在三个粗到细基准测试中优于基线，最大成功率比基线高81%。此外，DiSPo通过在不那么关键的区域生成粗动作来提高推理效率。最后，我们通过仿真和现实世界操作任务展示了动作的可扩展性。代码和视频可在https://robo-dispo.github.io获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
