arXiv:2605.30740v1 Announce Type: cross Abstract: 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. Specifically, a vision-based perceiver generates
The proliferation of advanced AI models and the increasing demand for autonomous systems in unstructured environments are driving innovation in robotic manipulation.
This development indicates progress towards more robust and versatile robotic systems capable of operating safely in diverse settings, addressing a critical bottleneck in robot deployment.
Robots will be able to interact with a wider variety of articulated objects more reliably and safely, reducing the need for highly structured environments or human intervention.
- · Robotics companies
- · Logistics and manufacturing sectors
- · Service industries using robots
- · AI researchers
Improved robotic efficiency and reduced damage in manipulation tasks.
Accelerated adoption of robots in complex operational environments like elder care or disaster relief.
Enhanced human-robot collaboration due to increased safety and capability, leading to new forms of assistive technology.
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Read at arXiv cs.AI