ShakingBot: Dynamic Manipulation for Bagging

TL;DR

ShakingBot introduces a dynamic manipulation framework for robotic bagging, utilizing perception and shaking actions to open deformable bags effectively, demonstrating success across various configurations and outperforming static methods.

Contribution

The paper presents a novel dynamic manipulation approach, ShakingBot, for robotic bagging that effectively opens deformable bags using shaking actions and perception, outperforming static methods.

Findings

Achieved a 21/33 success rate in bag opening and item insertion.

Demonstrated generalization across different bag sizes, patterns, and colors.

Showed dynamic shaking actions outperform quasi-static manipulation.

Abstract

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to account for the crumpled configuration.Then, we insert the items and lift the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking actions compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag's size, pattern, and color.