ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots
Milad Shafiee,Guillaume Bellegarda,Auke Ijspeert,Milad Shafiee,Guillaume Bellegarda,Auke Ijspeert
Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated for every new robot, where hyperparameters and reward function weights must be re-tuned to maximize performance for each new system. Alternatively, attempting to train a single policy to accommodate different robot sizes,...
