用学习压缩的有限元模型，实现软机器人高效建模与实时控制。
Modeling, Embedded Control and Design of Soft Robots using a Learned Condensed FEM Model
- 通过学习压缩有限元模型，统一处理多种驱动和环境接触。
- 模型推理速度快、内存占用低，支持实时嵌入式控制。
- 可微分特性适用于设计优化与参数校准，适配多形态软体机器人。
有限元方法（FEM）是预测软体机器人行为的强大工具，但其计算耗时限制了实际应用。本文提出一种基于FEM模型压缩的学习方法，可处理多种执行器与环境接触。该紧凑模型能作为统一框架，推导机器人的正逆运动学。基于[11]的直觉，该模型被用于建模、控制与设计软机械臂。首先，在涉及机械接触耦合的位置调整与操作任务中，展示了方法的适应性与通用性。其次，利用模型低内存消耗与高预测速度，实现无需在线昂贵FEM模拟的实时嵌入式控制。最后，利用学习后的压缩FEM模型对设计变化的捕捉能力及其可微性，应用于校准与设计优化任务。
原文摘要 · Abstract (English)
The Finite Element Method (FEM) is a powerful modeling tool for predicting soft robots' behavior, but its computation time can limit practical applications. In this paper, a learning-based approach based on condensation of the FEM model is detailed. The proposed method handles several kinds of actuators and contacts with the environment. We demonstrate that this compact model can be learned as a unified model across several designs and remains very efficient in terms of modeling since we can deduce the direct and inverse kinematics of the robot. Building upon the intuition introduced in [11], the learned model is presented as a general framework for modeling, controlling, and designing soft manipulators. First, the method's adaptability and versatility are illustrated through optimization based control problems involving positioning and manipulation tasks with mechanical contact-based coupling. Secondly, the low memory consumption and the high prediction speed of the learned condensed model are leveraged for real-time embedding control without relying on costly online FEM simulation. Finally, the ability of the learned condensed FEM model to capture soft robot design variations and its differentiability are leveraged in calibration and design optimization applications.
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