通过采样优化实现算法与非可微控制模式的最优切换
Sample-Based Hybrid Mode Control: Asymptotically Optimal Switching of Algorithmic and Non-Differentiable Control Modes
- 将控制模式切换建模为整数优化问题，采样搜索最优解
- 在真实机器人上实现长期规划与高频控制的实时响应切换
- 适用于需复杂行为合成的高难度机器人任务
本文研究了一种基于样本的混合模式控制方法，用于处理非可微与算法型混合控制模式之间的切换问题。将一组混合控制模式视为一个基于整数的优化问题，通过选择应用何种模式、何时切换以及在某一模式下的持续时间来求解。提出一种基于采样的变体方法，高效搜索整数解空间以获得最优解。该方法在多个机器人相关任务中展现出强性能保证，并能合成复杂算法与策略，实现复合行为，完成挑战性任务。最后，在需要在长期规划与高频控制之间进行反应式切换的真实机器人案例中验证了其有效性。
原文摘要 · Abstract (English)
This paper investigates a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes. Our approach reasons about a set of hybrid control modes as an integer-based optimization problem where we select what mode to apply, when to switch to another mode, and the duration for which we are in a given control mode. A sample-based variation is derived to efficiently search the integer domain for optimal solutions. We find our formulation yields strong performance guarantees that can be applied to a number of robotics-related tasks. In addition, our approach is able to synthesize complex algorithms and policies to compound behaviors and achieve challenging tasks. Last, we demonstrate the effectiveness of our approach in real-world robotic examples that require reactive switching between long-term planning and high-frequency control.
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。