Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
From the abstract
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a systematic framework for stable and sample-efficient real-world fine-tuning of complex MoMa systems.
From the abstract. Our summary is in progress.
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