AI RESEARCH
GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation
arXiv CS.AI
•
ArXi:2605.30740v1 Announce Type: cross Articulated object manipulation is a unique challenge for service robots. Existing methods employ end-to-end policy learning, visionmotion planning, and large-language/visual-language model (LLM/VLM), but often overlook the diversity of articulated objects and the complexity of interactions between end-effector and handle, leading to limited generalization and destructive collisions. To address this, we propose GSAM, a generalizable and safe robotic framework for articulated object manipulation.