LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors
Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective. We instead treat these functions through distinct mechanisms: rewards for task specification, constraints for operational limits, energy minimization for gait preference, and exteroceptive perception for adapting energy use to terrain difficulty. We show that these components jointly enable efficient, terrain-adaptive locomotion, and that removing each component exposes a distinct failure mode. Our formulation removes explicit gait priors (including air-time, contact-count...
| Authors | Loukas Kordos, Leonard T. Franz, Simon Rappenecker et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 June 2026 |
| Last updated at source | 12 October 2026 |
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