New method enables sim-to-real transfer for muscle-actuated robots

Researchers have developed a new pipeline for transferring robot control policies from simulation to real-world muscle-actuated robots. This method, called Generalized Actuator Network (GenAN), uses neural networks to model the complex, non-linear dynamics of pneumatic artificial muscles, overcoming challenges like friction and hysteresis that previously hindered sim-to-real transfer. The system was successfully demonstrated on a four-degree-of-freedom robot arm, enabling precise execution of tasks such as goal-reaching and table tennis, trained entirely in simulation. AI

IMPACT Enables more complex robotic tasks by improving sim-to-real transfer for muscle-actuated systems.

RANK_REASON The cluster contains an academic paper detailing a new method for robotics research. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Generalized Actuator Network (GenAN)
• Jan Schneider
• PAMY2

• paper
• other

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