Scalable, principled zero-shot learning of skill transitions across mixed terrains

Determine whether there exists a scalable, principled reinforcement learning approach that enables zero-shot generalization for challenging skill transitions in legged locomotion across mixed terrains, such as decelerating on sparse regions before initiating a climbing maneuver while maintaining precise footholds.

Background

While the controllers perform well on training terrains, the authors observe increased failures on unseen test terrains, especially during transitions between distinct skills (e.g., moving from sparse footholds to climbing).

Although ad hoc finetuning may address particular instances, the authors explicitly state that it is unknown whether a scalable, principled method can learn these transitions in a way that generalizes zero-shot to previously unseen combinations.

References

While these specific cases could likely be addressed by finetuning, it remains unclear whether there is a scalable, principled approach for learning such challenging skill transitions with zero-shot generalization.

— AME-2: Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding  (2601.08485 - Zhang et al., 13 Jan 2026) in Discussion — Section 8.4 (Towards Higher Success Rates on Unseen Terrains)