The accurate simulation of deformable linear object (DLO) dynamics is
challenging if the task at hand requires a human-interpretable and
data-efficient model that also yields fast predictions. To arrive at such
model, we draw inspiration from the rigid finite element method (R-FEM) and
model a DLO as a serial chain of rigid bodies whose internal state is unrolled
through time by a dynamics network. As this state is not observed directly, the
dynamics network is trained jointly with a physics-informed encoder mapping
observed motion variables to the body chain's state. To encourage that the
state acquires a physically meaningful representation, we leverage the forward
kinematics (FK) of the underlying R-FEM model as a decoder. We demonstrate in a
robot experiment that this architecture - being termed "Finite element inspired
network" - forms an easy to handle, yet capable DLO dynamics model yielding
physically interpretable predictions from partial observations.
The project code is available at: https://tinyurl.com/fei-networks

本研究提出了一个基于刚性有限元方法和动态网络的 “有限元启发网络” 架构来模拟可变形线性物体的动力学，将观测到的运动变量映射到身体链的状态，从而得出一种易于处理且能够从部分观测中获得物理可解释性预测的 DLO 动力学模型。

有限元启发网络：从部分观测中学习符合物理原理的可变形物体动力学

Finite element inspired networks: Learning physically-plausible  deformable object dynamics from partial observations

This paper presents Latent Sampling-based Motion Planning (L-SBMP), a
methodology towards computing motion plans for complex robotic systems by
learning a plannable latent representation. Recent works in control of robotic
systems have effectively leveraged local, low-dimensional embeddings of
high-dimensional dynamics. In this paper we combine these recent advances with
techniques from sampling-based motion planning (SBMP) in order to design a
methodology capable of planning for high-dimensional robotic systems beyond the
reach of traditional approaches (e.g., humanoids, or even systems where
planning occurs in the visual space). Specifically, the learned latent space is
constructed through an autoencoding network, a dynamics network, and a
collision checking network, which mirror the three main algorithmic primitives
of SBMP, namely state sampling, local steering, and collision checking.
Notably, these networks can be trained through only raw data of the system's
states and actions along with a supervising collision checker. Building upon
these networks, an RRT-based algorithm is used to plan motions directly in the
latent space - we refer to this exploration algorithm as Learned Latent RRT
(L2RRT). This algorithm globally explores the latent space and is capable of
generalizing to new environments. The overall methodology is demonstrated on
two planning problems, namely a visual planning problem, whereby planning
happens in the visual (pixel) space, and a humanoid robot planning problem.

通过学习可规划的潜在表示，本文提出了基于潜在采样的动作规划 (L-SBMP) 的方法，该方法结合了控制机器人系统的最新进展和采样式运动规划 (SBMP) 的技术，可以处理高维度机器人系统的动作规划问题，同时采用了三个算法基本原则，即状态采样，局部转向和碰撞检查。