Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance.

arXiv:2610.20607v1 Announce Type: cross 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 hete