RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning
RLMM-Flow proposes a mobile manipulation framework combining expert flow-policy pretraining with latent-space reinforcement learning post-training. It learns multimodal whole-body motion priors from demonstrations, then uses a latent steering network to guide initial noise toward high-value actions. Experiments show significant improvements in task success, collision avoidance, and trajectory quality.
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[Submitted on 29 Jul 2026]
Title:RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning
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Abstract: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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.26460 [cs.RO]
(or arXiv:2607.26460v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.26460
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shuhang Wang [view email] [v1] Wed, 29 Jul 2026 04:22:46 UTC (13,043 KB)
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