通过Koopman算子基于模型预测控制实现双足机器人安全导航
Safe Navigation of Bipedal Robots via Koopman Operator-Based Model Predictive Control
AI总结：
本文提出基于Koopman算子的模型预测控制方法，用于提升双足机器人在复杂环境中的安全导航性能。
摘要：
动力学非线性长期以来一直是机器人领域的主要挑战，常常导致现有控制算法性能显著下降。例如，双足机器人的导航在简单的速度指令下也会表现出非线性行为，因为其实际动力学由复杂的全身运动和离散接触决定。在本工作中，我们提出了一种受Koopman算子理论启发的安全导航框架。我们首先使用深度强化学习训练低级运动策略，然后通过在高维提升空间中学习线性化动力学来捕捉其低频基础动力学。然后，我们的模型预测控制器（MPC）通过标准二次目标和提升空间中的线性动力学约束高效优化控制信号。我们证明Koopman模型比基线方法更准确地预测双足机器人的轨迹。我们还表明，所提出的导航框架在密集环境中狭窄通道中实现了更高的安全性和成功率。
英文摘要：
Nonlinearity in dynamics has long been a major challenge in robotics, often causing significant performance degradation in existing control algorithms. For example, the navigation of bipedal robots can exhibit nonlinear behaviors even under simple velocity commands, as their actual dynamics are governed by complex whole-body movements and discrete contacts. In this work, we propose a safe navigation framework inspired by Koopman operator theory. We first train a low-level locomotion policy using deep reinforcement learning, and then capture its low-frequency, base-level dynamics by learning linearized dynamics in a high-dimensional lifted space. Then, our model-predictive controller (MPC) efficiently optimizes control signals via a standard quadratic objective and the linear dynamics constraint in the lifted space. We demonstrate that the Koopman model more accurately predicts bipedal robot trajectories than baseline approaches. We also show that the proposed navigation framework achieves improved safety with better success rates in dense environments with narrow passages.
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