Visual terrain information and energy-efficient traversability

Determine whether incorporating visual terrain information into the hierarchical reinforcement-learning controller can extend the traversable environments of quadrupedal robots while preserving the low cost of transport achieved by the current proprioceptive formulation.

Background

The proposed hierarchical reinforcement-learning framework uses proprioceptive observations for energy-aware gait adaptation across flat, rough, and inclined terrains. The authors identify vision-based perception and adaptive high-level update rates as directions for future development, particularly for challenging environments containing large irregular obstacles, stair-like structures, and steep slopes.

The unresolved issue is whether exteroceptive terrain information can expand the range of traversable environments and allow the high-level policy to exploit a broader gait-command space without sacrificing the low cost of transport obtained by the proprioceptive design. The authors also note a potential trade-off because enlarging the high-level policy's scope may increase energy consumption.

References

Therefore, an important objective is to determine whether visual terrain information can extend traversable environments while preserving the low CoT achieved by the current proprioceptive formulation.

— Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains  (2610.10297 - Issa et al., 7 Oct 2026) in Section Discussion and Conclusion