5 papers · 1 filter
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…
State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials
Edgar Granados, Patrick Meng, Charles Tang +4
Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable continuous-time estimation of al…
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…
Impact-resistant, autonomous robots inspired by tensegrity architecture
William R. Johnson, Xiaonan Huang, Shiyang Lu +4
Future robots will navigate perilous, remote environments with resilience and autonomy. Researchers have proposed building robots with compliant bodies to enhance robustness, but t…
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…