RANDPOL: Parameter-Efficient End-to-End Quadruped Locomotion via Randomized Policy Learning
arXiv:2505.19054v2 Announce Type: replace
Abstract: Modern learning-based locomotion controllers typically rely on fully trainable deep neural networks with a large number of parameters. This paper studies a different design point for end-to-end contr…