用神经微分方程联合学习连续体机器人形状与控制策略。
A Synergistic Framework for Learning Shape Estimation and Shape-Aware Whole-Body Control Policy for Continuum Robots
- 双神经微分方程协同：形状估计与控制优化相互增强。
- 仿真与真实环境均实现高精度轨迹跟踪与避障。
- 适合需精确形变感知的柔性机器人系统研究者。
本文提出一种新颖的协同框架，用于学习腱驱动连续体机器人的形状估计与形变感知的整体控制策略。该方法通过两个增强型神经常微分方程（ANODE）——形状-神经微分方程（Shape-NODE）与控制-神经微分方程（Control-NODE）——的交互，实现连续的形状估计与形变感知控制。Shape-NODE 融合柯塞拉杆理论先验知识，可自适应模型失配；Control-NODE 则利用形状信息，以模型预测控制（MPC）方式优化整体控制策略。该统一框架有效克服了现有数据驱动方法在形状感知不足和复杂非线性动态建模方面的局限。在仿真与真实环境中的大量评估表明，该方法在形状估计、轨迹跟踪与障碍物避让方面表现稳健，显著优于当前最优的端到端、神经微分方程及循环神经网络（RNN）模型，尤其在跟踪精度与泛化能力上优势明显。
原文摘要 · Abstract (English)
In this paper, we present a novel synergistic framework for learning shape estimation and a shape-aware whole-body control policy for tendon-driven continuum robots. Our approach leverages the interaction between two Augmented Neural Ordinary Differential Equations (ANODEs) -- the Shape-NODE and Control-NODE -- to achieve continuous shape estimation and shape-aware control. The Shape-NODE integrates prior knowledge from Cosserat rod theory, allowing it to adapt and account for model mismatches, while the Control-NODE uses this shape information to optimize a whole-body control policy, trained in a Model Predictive Control (MPC) fashion. This unified framework effectively overcomes limitations of existing data-driven methods, such as poor shape awareness and challenges in capturing complex nonlinear dynamics. Extensive evaluations in both simulation and real-world environments demonstrate the framework's robust performance in shape estimation, trajectory tracking, and obstacle avoidance. The proposed method consistently outperforms state-of-the-art end-to-end, Neural-ODE, and Recurrent Neural Network (RNN) models, particularly in terms of tracking accuracy and generalization capabilities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。