PolyMorph: Learning Locomotion Policy for Cross-Embodiment Adaptation

Reinforcement learning has enabled robust locomotion for legged robots, but learned policies are typically tied to a specific robot morphology and require additional engineering when the number of legs, actuators, or mechanical properties change. We present PolyMorph, a morphology-conditioned framework that learns a single locomotion policy for heterogeneous legged robots.

From the abstract. Our summary is in progress.