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

TL;DR

CageCoOpt introduces a hierarchical reinforcement and Bayesian optimization framework that co-optimizes robot morphology and control policies using caging principles to improve robustness in manipulation tasks under uncertainty.

Contribution

The paper presents a novel integrated framework for joint morphology and policy optimization using caging metrics, bridging a gap in existing research.

Findings

Co-optimization improves success rates under uncertainties.

Caging-guided co-optimization enhances manipulation robustness.

Framework outperforms separate optimization approaches.

Abstract

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. The framework employs reinforcement learning for policy optimization at the lower level and multi-task Bayesian optimization for morphology optimization at the upper level. A robustness metric in caging, Minimum Escape Energy, is incorporated into the objectives of both levels to promote caging configurations and enhance manipulation robustness. The evaluation results through four manipulation tasks demonstrate that co-optimizing morphology and policy improves success rates under uncertainties, establishing caging-guided co-optimization as a viable approach for robust manipulation.