Robotics

SoMo-RL is an open-source toolkit for developing and evaluating control policies for soft robots. Using the SoMo simulation framework and SoMoGym library, SoMo-RL permits experiments on, e.g., the effects of varying control and robot design parameters, and enables the use of RL for such systems. SoMo-RL builds off the functionality of SoMoGym, providing a straightforward system for training RL policies on SoMoGym environments, managing experiments at scale, and analyzing RL results. In SoMo-RL, experiments are highly customizable, allowing for complete modification of learning hyperparameters and environment parameters through a single configuration file.

https://github.com/GrauleM/somo-rl?tab=readme-ov-file