端到端QP策略：鲁棒控制与机器人学习的统一视角
End-to-end QP-based policies: A unified perspective on robust control and robot learning
AI总结：
本文提出端到端QP策略框架，统一鲁棒控制与机器人学习，支持自动调优与黑盒构建，并在仿真和硬件上验证其广泛适用性。
AI中文摘要：
我们提出了一种端到端的基于二次规划（QP）的策略框架，该框架能够以最小的领域特定设计实现系统性的策略构建，同时保留基于模型控制的透明性和可解释性。所提出的策略表示支持基于领域随机化的模型自动调优，其中策略参数在扰动和模型变化的分布上进行优化，也支持黑盒策略构建，其中问题以（可能）未知的模型形式表述，无需显式定义状态、输入或底层系统动力学。我们建立了与现有策略表示和控制范式（包括鲁棒控制、多层感知机和机器人学习）的联系，并将我们的端到端QP策略解释为这些方法的共同抽象。我们在仿真和硬件上验证了所提出的框架，展示了涵盖鲁棒性、自动策略调优以及未知系统动力学下的控制等广泛的应用场景。
英文摘要：
We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the transparency and interpretability of model-based control. The proposed policy representation supports both domain-randomized model-based auto-tuning, where policy parameters are optimized over distributions of disturbances and model variations, and black-box policy construction, where the problem is formulated in terms of a (possibly) unknown model without requiring explicit notions of states, inputs, or the underlying system dynamics. We establish connections to existing policy representations and control paradigms, including robust control, multilayer perceptrons, and robot learning, and interpret our end-to-end QP policies as a common abstraction of these approaches. We validate the resulting framework in both simulation and hardware, demonstrating a broad range of applications spanning robustness, automatic policy tuning, and control under unknown system dynamics.
