4 papers · 1 filter
Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model
Nelson Chen, Patrick Meng, Charles Tang +5
Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich dynamics and partial observab…
CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics
Nelson Chen, William R. Johnson, Rebecca Kramer-Bottiglio +2
General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depe…
An Open-Source, Reproducible Tensegrity Robot that can Navigate Among Obstacles
William R. Johnson, Patrick Meng, Nelson Chen +5
Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, couple…
Learning Differentiable Tensegrity Dynamics using Graph Neural Networks
Nelson Chen, Kun Wang, William R. Johnson +3
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 arra…