Factor graphs and Chebyshev polynomials enable robust continuous-time state and trajectory estimation for tensegrity robots by fusing RGB-D camera and cable sensor data.
Learning differentiable tensegrity dynamics using graph neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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A GNN-augmented SAC policy that encodes tensegrity topology as a graph improves sample efficiency and enables zero-shot sim-to-real locomotion on a 3-bar tensegrity robot.
citing papers explorer
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State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials
Factor graphs and Chebyshev polynomials enable robust continuous-time state and trajectory estimation for tensegrity robots by fusing RGB-D camera and cable sensor data.
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Morphology-Aware Graph Reinforcement Learning for Tensegrity Robot Locomotion
A GNN-augmented SAC policy that encodes tensegrity topology as a graph improves sample efficiency and enables zero-shot sim-to-real locomotion on a 3-bar tensegrity robot.