PartPose: Attentive 6D Pose Estimation by Focusing on Graspable Parts of Multi-Part Deformable Objects

Abstract

This study tackles robotic picking of multi-part deformable objects--common in warehouses yet underexplored in the literature--such as cable-attached appliances and pouch drinks, which comprise both rigid and deformable components. Their deformability poses a challenge to model-based 6D pose estimators, such as FoundationPose, that assume rigid bodies. To address this, we present PartPose, which estimates the 6D pose of the multi-part deformable objects by focusing on the rigid components. PartPose uses Bayesian optimization to select an appropriate region of interest (ROI) and then estimates its pose with a render-and-compare pipeline. We evaluate pose-estimation and picking success rates on nine multi-part deformable objects, counting a pose estimate as successful if the translational error is <30 mm and the rotational error is <0.3 radians. PartPose significantly outperforms a FoundationPose baseline, achieving success rates of 98.2% (translational), 96.4% (rotational), and 87.2% (picking), versus 47.9%, 35.9%, and 22.8%, respectively. Moreover, PartPose generalizes category-level semantic knowledge to new instances within the same category without performance degradation when those instances have semantically similar components. This capability is crucial for large logistics centers that handle diverse and novel objects.