GeAN learns actuator dynamics from position trajectories to enable successful sim-to-real transfer of goal-reaching and ball-in-a-cup policies on a 4-DoF pneumatic muscle-actuated robot, reported as the first such transfer for this system type.
An Efficient Learning Control Framework With Sim-to- Real for String-Type Artificial Muscle-Driven Robotic Systems,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
GeAN learns actuator dynamics from position trajectories to enable successful sim-to-real transfer of goal-reaching and ball-in-a-cup policies on a 4-DoF pneumatic muscle-actuated robot, reported as the first such transfer for this system type.