Parameterized locomotion RL paper transfers robust walking policies to Cassie

Evidence notes

• The paper trains robust locomotion policies in simulation using domain randomization and transfers them to the physical Cassie robot.
• The learned policies support dynamic behaviors such as velocity tracking, walking-height variation, and yaw turning.
• It adds a high-quality Cassie result for model-free RL control beyond hand-tuned model-based walking.