Shuo Cheng, Kaichun Mo and Lin Shao, Learning to Regrasp by Learning to Place, Conference on Robot Learning (CoRL) 2021

Abstract:

In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses. Regrasping is needed whenever a robot’s current grasp pose fails to perform desired manipulation tasks. Endowing robots with such an ability has applications in many domains such as manufacturing or domestic services. Yet, it is a challenging task due to the large diversity of geometry in everyday objects and the high dimensionality of the state and action space. In this paper, we propose a system for robots to take partial point clouds of an object and the supporting environment as inputs and output a sequence of pick-and-place operations to transform an initial object grasp pose to the desired object grasp poses. The key technique includes a neural stable placement predictor and a regrasp graph based solution through leveraging and changing the surrounding environment. We introduce a new and challenging synthetic dataset for learning and evaluating the proposed approach. We demonstrate the effectiveness of our proposed system with both simulator and real-world experiments.

Bibtex: 

@inProceedings{cheng2021regrasp,
    title={Learning to Regrasp by Learning to Place},
    author={Cheng, Shuo and Mo, Kaichun and Shao, Lin},
    year={2021},
    booktitle={Conference on Robot Learning (CoRL)}
}