CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.
Pvp: Data-efficient humanoid robot learning with proprioceptive-privileged contrastive representations.arXiv preprint arXiv:2512.13093, 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.