Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
By Zhefei Gong · Paper · cs.RO
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 R
Tags: Cs.ro