Differentiable Object Pose Connectivity Metrics for Regrasp Sequence Optimization

TL;DR

This paper introduces a differentiable, energy-based approach for multi-step regrasp planning that improves robustness and generalization in object pose connectivity optimization.

Contribution

It proposes a novel implicit framework using energy-based models and gradient optimization for continuous regrasp sequence planning.

Findings

The method provides smooth, informative gradients for planning.

It generalizes to unseen grasp poses and cross-end-effector transfer.

Adaptive deepening improves planning efficiency.

Abstract

Regrasp planning is often required when one pick-and-place cannot transfer an object from an initial pose to a goal pose while maintaining grasp feasibility. The main challenge is to reason about shared-grasp connectivity across intermediate poses, where discrete search becomes brittle. We propose an implicit multi-step regrasp planning framework based on differentiable pose sequence connectivity metrics. We model grasp feasibility under an object pose using an Energy-Based Model (EBM) and leverage energy additivity to construct a continuous energy landscape that measures pose-pair connectivity, enabling gradient-based optimization of intermediate object poses. An adaptive iterative deepening strategy is introduced to determine the minimum number of intermediate steps automatically. Experiments show that the proposed cost formulation provides smooth and informative gradients, improving planning robustness over other alternatives. They also demonstrate generalization to unseen grasp poses and cross-end-effector transfer, where a model trained with suction constraints can guide parallel gripper grasp manipulation. The multi-step planning results further highlight the effectiveness of adaptive deepening and minimum-step search.