将控制输入融入谱子流形，实现连续体机器人的高精度实时控制。
Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots
- 在状态中显式加入控制输入，捕捉非线性状态-输入耦合关系。
- 相比现有方法，开环预测误差降低40%，闭环跟踪误差减少52%。
- 无需额外校准步骤，适合实际硬件部署，尤其适用于肌腱驱动机器人。
连续体机器人具有高维非线性动力学，常与执行机构强耦合。谱子流形（SSM）降维已成为降低高维非线性系统复杂度的主流方法。本文提出的控制增强型谱子流形（caSSM）通过在状态表示中显式纳入控制输入，使模型能捕捉非线性状态-输入耦合。训练仅依赖于执行器增强状态的受控衰减轨迹，避免了以往基于SSM的控制方法所需的额外执行器标定步骤。我们为肌腱驱动躯干机器人学习了一个紧凑的caSSM模型，实现了实时控制，开环预测误差比现有方法降低40%。在基于模型预测控制（MPC）的闭环实验中，caSSM将跟踪误差降低52%，优于基于Koopman和传统SSM的MPC方法，并具备在真实硬件上部署的可行性。
原文摘要 · Abstract (English)
Continuum robots exhibit high-dimensional, nonlinear dynamics which are often coupled with their actuation mechanism. Spectral submanifold (SSM) reduction has emerged as a leading method for reducing high-dimensional nonlinear dynamical systems to low-dimensional invariant manifolds. Our proposed control-augmented SSMs (caSSMs) extend this methodology by explicitly incorporating control inputs into the state representation, enabling these models to capture nonlinear state-input couplings. Training these models relies solely on controlled decay trajectories of the actuator-augmented state, thereby removing the additional actuation-calibration step commonly needed by prior SSM-for-control methods. We learn a compact caSSM model for a tendon-driven trunk robot, enabling real-time control and reducing open-loop prediction error by 40% compared to existing methods. In closed-loop experiments with model predictive control (MPC), caSSM reduces tracking error by 52%, demonstrating improved performance against Koopman and SSM based MPC and practical deployability on hardware continuum robots.
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