用物理模型+强化学习，让机器人更抗干扰。
Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback
- 结合牛顿-欧拉方程构建简化动力学模型，避免复杂非线性建模。
- 通过预测误差反馈实现闭环控制，在无人机和四足机器人上性能领先。
- 适合需要高鲁棒性的真实场景机器人控制，尤其擅长模拟到现实的迁移。
在复杂环境中应对扰动的精准运动控制仍是机器人领域的重大挑战。传统基于模型的方法难以处理非线性和未结构化扰动，而基于强化学习（RL）的方法在遭遇未知场景时往往脆弱。本文提出一种新框架——神经内部模型控制（Neural Internal Model Control），将基于模型的控制与基于RL的控制相结合，以提升鲁棒性。该框架通过应用牛顿-欧拉方程对刚体动力学进行建模，简化了预测模型，无需捕捉复杂的高维非线性特征。内部模型融合无模型强化学习算法与预测误差反馈，形成闭环控制结构，显著增强系统的鲁棒性和泛化能力。我们在四旋翼无人机和四足机器人上验证了该框架的有效性，性能优于现有先进方法。此外，在带有绳悬负载的四旋翼无人机上开展的真实世界部署，进一步证明了其在模拟到现实迁移中的强鲁棒性。代码已开源：https://github.com/thu-uav/NeuralIMC。
原文摘要 · Abstract (English)
Accurate motion control in the face of disturbances within complex environments remains a major challenge in robotics. Classical model-based approaches often struggle with nonlinearities and unstructured disturbances, while RL-based methods can be fragile when encountering unseen scenarios. In this paper, we propose a novel framework, Neural Internal Model Control, which integrates model-based control with RL-based control to enhance robustness. Our framework streamlines the predictive model by applying Newton-Euler equations for rigid-body dynamics, eliminating the need to capture complex high-dimensional nonlinearities. This internal model combines model-free RL algorithms with predictive error feedback. Such a design enables a closed-loop control structure to enhance the robustness and generalizability of the control system. We demonstrate the effectiveness of our framework on both quadrotors and quadrupedal robots, achieving superior performance compared to state-of-the-art methods. Furthermore, real-world deployment on a quadrotor with rope-suspended payloads highlights the framework's robustness in sim-to-real transfer. Our code is released at https://github.com/thu-uav/NeuralIMC.
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