Learning Differentiable Tensegrity Dynamics using Graph Neural Networks

Nelson Chen, Kun Wang, William R. Johson III, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya

Keywords: graph neural networks, differentiable simulation, tensegrity robots

TL;DR: This paper describes a graph neural network method to learn Tensegrity robot dynamics.

Abstract: Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their compliance and high number of degrees of freedom. To address this issue, prior work has introduced a differentiable physics engine designed for tensegrity robots based on first principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages their natural graph-like cable connec- tivity between end caps of rigid rods. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, for which the robot state is only partially observable. When compared against direct applications of recent mesh-based graph neural network simulators, the proposed approach is computationally more efficient, both for training and inference, while achieving higher accuracy. Code and data are available at https://github.com/nchen9191/tensegrity_gnn_simulator_public

Video: https://www.youtube.com/watch?v=dPeWT_WaEBA

Code: https://github.com/nchen9191/tensegrity_gnn_simulator_public

Student Paper: yes

Submission Number: 510