Dynamic isotropy, quantifying uniform center-of-mass acceleration capability, improves robot performance and enables omnidirectional locomotion, terrain traversal, and failure resilience in a spherical robot design.
Adaptive tensegrity locomotion: Controlling a compliant icosahedron with symmetry-reduced reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2verdicts
UNVERDICTED 2representative citing papers
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
-
Extreme dynamic symmetry enables omnidirectional and multifunctional robots
Dynamic isotropy, quantifying uniform center-of-mass acceleration capability, improves robot performance and enables omnidirectional locomotion, terrain traversal, and failure resilience in a spherical robot design.
-
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.