用数学方法让双足机器人更安全地穿越狭窄空间
Safe Navigation of Bipedal Robots via Koopman Operator-Based Model Predictive Control
- 将复杂运动转为线性模型，提升控制精度
- 在密集环境中导航成功率显著提高
- 适合研究机器人安全控制的学者与工程师
机器人动力学的非线性长期制约控制性能，双足机器人在简单速度指令下仍可能表现出复杂运动。本文提出基于Koopman算子理论的安全导航框架：先用深度强化学习训练底层步态策略，再在高维升维空间中学习其低频基线性动态；随后通过标准二次目标和线性约束的模型预测控制器（MPC）高效优化控制信号。实验表明，该方法比基线模型更准确预测双足机器人轨迹，在密集环境与窄通道中实现更高导航成功率。
原文摘要 · Abstract (English)
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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