6 citations · 18 across the 16 of their papers we have counts for
9 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…
A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data
Kun Wang, Mridul Aanjaneya, Kostas Bekris
Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differe…
Spring-Rod System Identification via Differentiable Physics Engine
Kun Wang, Mridul Aanjaneya, Kostas Bekris
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dy…
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots
Kun Wang, Mridul Aanjaneya, Kostas Bekris
Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but…
Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics
Yanshi Luo, Abdeslam Boularias, Mridul Aanjaneya
We present the design of a low-cost wheeled mobile robot, and an analytical model for predicting its motion under the influence of motor torques and friction forces. Using our prop…
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
Kun Wang, Mridul Aanjaneya, Kostas Bekris
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dy…