用分层线性参数变化系统提升高维演示学习的稳定性与效率
Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
- 将复杂动态系统分解为可组合的线性参数变化子系统
- 避免求解非凸双线性矩阵不等式，显著降低计算开销
- 适合需快速泛化高维机器人操作任务的研究者
从示范学习（LfD）技术使机器人能够从用户示范中学习并泛化任务，无需终端用户具备编程能力。目前实现机器人LfD的一种成熟方法是将示范编码为稳定的动力系统（DS）。然而，寻找稳定动力系统需要求解带有双线性矩阵不等式（BMI）约束的优化问题，这是一个非凸问题，其计算资源消耗随标量约束和变量数量增加而急剧上升，且易受浮点数误差等数值问题影响。为此，我们提出一种新型组合式方法，通过构建可组合的线性参数变化动力系统，增强稳定动力系统学习的适用性与可扩展性。
原文摘要 · Abstract (English)
Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, finding a stable dynamical system entails solving an optimization problem with bilinear matrix inequality (BMI) constraints, a non-convex problem which, depending on the number of scalar constraints and variables, demands significant computational resources and is susceptible to numerical issues such as floating-point errors. To address these challenges, we propose a novel compositional approach that enhances the applicability and scalability of learning stable DSs with BMIs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。