Constrained skill discovery paper deploys unsupervised locomotion on ANYmal

Evidence notes
- The paper proposes constrained unsupervised skill discovery for quadruped locomotion using a latent representation over states.
- The authors report deployment of learned policies on a real ANYmal quadruped robot for zero-shot Cartesian-state reaching.
Company context
ANYbotics develops autonomous legged robots for industrial inspection in complex and hazardous environments. Its ANYmal platform targets repeatable monitoring, inspection and safety workflows.