RLMM-Flow: 潜在空間強化学習を用いたフローベース移動操作フレームワーク
RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning
移動操作ロボットの全身動作生成を、専門家デモからのフローポリシー事前学習と潜在空間での強化学習後調整を組み合わせて改善するフレームワークを提案した。
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著者: Shuhang Wang, Ziming Li, Hui Cheng
分類: cs.RO
原文アブストラクト
Mobile manipulation requires generating whole-body action chunks that jointly satisfy goal reaching, collision avoidance, base kinematic constraints, manipulator joint limits, and trajectory smoothness. Flow-based generative policies provide an efficient paradigm for learning multimodal and temporally consistent motion priors from expert demonstrations, but imitation-only training cannot improve policy quality beyond the demonstration distribution. We propose RLMM-Flow, a flow-based mobile manipulation framework that combines expert flow-policy pretraining with latent-space reinforcement learning post-training. The framework first learns a flow policy that captures a multimodal whole-body motion prior from expert demonstrations. The pretrained flow policy is then frozen, while a latent steering network steers its initial noise toward higher-value action chunks. To stabilize high-dimensional latent optimization, we warm up an action-space critic before jointly training the latent critic and latent actor, and introduce coarse-to-fine latent steering that progressively expands control from a horizon-shared latent representation to a full-dimensional residual representation. Experiments on mobile manipulation motion-planning benchmarks show that RLMM-Flow substantially improves task success, collision avoidance, and trajectory quality over imitation-only flow policies and existing reinforcement learning post-training baselines, while preserving fast flow-based inference.
