LAR-MoE: ロボット模倣学習における潜在整列ルーティングによるMixture of Experts
LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning
模倣学習で異なる動作モードを扱うため、教師なしスキル発見とポリシー学習を分離し、潜在空間の構造に基づいてエキスパートをルーティングするLAR-MoEを提案。LIBEROで95.2%の成功率を達成し、手術タスクでも教師なしで同等性能を示した。
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著者: Ariel Rodriguez, Chenpan Li, Lorenzo Mazza, Rayan Younis, Ortrun Hellig, Sebastian Bodenstedt, Martin Wagner, Stefanie Speidel
分類: cs.RO
原文アブストラクト
Imitation learning enables robots to acquire manipulation skills from demonstrations, yet deploying a policy across tasks with heterogeneous dynamics remains challenging, as models tend to average over distinct behavioral modes present in the demonstrations. Mixture-of-Experts (MoE) architectures address this by activating specialized subnetworks, but requires meaningful skill decompositions for expert routing. We introduce Latent-Aligned Routing for Mixture of Experts (LAR-MoE), a two-stage framework that decouples unsupervised skill discovery from policy learning. In pre-training, we learn a joint latent representation between observations and future actions through student-teacher co-training. In a post-training stage, the expert routing is regularized to follow the structure of the learned latent space, preventing expert collapse while maintaining parameter efficiency. We evaluate LAR-MoE in simulation and on hardware. On the LIBERO benchmark, our method achieves a 95.2% average success rate with 150M parameters. On a surgical bowel grasping and retraction task, LAR-MoE matches a supervised MoE baseline without requiring any phase annotations, and transfers zero-shot to ex vivo porcine tissue. Our findings suggest that latent-aligned routing provides a principled alternative to supervised skill decomposition, enabling structured expert specialization from unlabeled demonstrations.
