CageCoOpt: Enhancing Manipulation Robustness through Caging-Guided Morphology and Policy Co-Optimization

Understand

Uncertainties in contact dynamics and object geometry remain significant barriers to robust robotic manipulation.

• Caging mitigates these uncertainties by constraining an object's mobility without requiring precise contact modeling.
• However, existing caging research has largely treated morphology and policy optimization as separate problems, overlooking their inherent synergy.
• In this paper, we introduce CageCoOpt, a hierarchical framework that jointly optimizes manipulator morphology and control policy for robust manipulation.