Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Paper recorded by Signals 4 on 2026-10-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15
Category: cs.AI · 人工智能 · first seen 2026-10-15
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 M
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Cite this page: Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance: the #302 most recent of 960 cs.AI papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/hierarchical-residual-policy-learning-for-real-world-mobile-manipulation-with-sp.html
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