Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice due to inherent nonlinearities, friction, and hysteresis, which complicate modeling and control. So far, these challenges have hindered policy transfer from simulation to real systems. To bridge this gap, we propose a sim-to-real pipeline that learns a neural network model of this complex actuation and leverages established rigid body simulation for the arm dynamics and interactions with the environment. Our method, called Generalized Actuator Network (GenAN), enables actuation model identification across a wide r...
| Authors | Jan Schneider, Mridul Mahajan, Le Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 April 2026 |
| Last updated at source | 15 October 2026 |
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