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Paper ThAT7.2

Wang, Yue (Zhejiang University), Jie, Cai (Zhejiang University), Wang, Yabiao (Zhejiang University), Hu, Youzhong (Zhejiang University), Xiong, Rong (Zhejiang University), Liu, Yong (Zhejiang University), Zhang, Jiafan (ABB Corporate Research Center, China), Qi, Liwei (abb)

Probabilistic Graph Based Spatial Assembly Relation Inference for Programming of Assembly Task by Demonstration

Scheduled for presentation during the Regular session "Perception for Grasping and Manipulation 1" (ThAT7), Thursday, October 1, 2015, 08:45−09:00, Saal B3

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on May 25, 2019

Keywords Perception for Grasping and Manipulation, Learning from Demonstration

Abstract

In robot programming by demonstration (PBD) for assembly tasks, one of the important topics is to inference the poses and spatial relations of parts during the demonstration. In this paper, we propose a world model called assembly graph (AG) to achieve this task. The model is able to represent the poses of all parts, the relations, observations provided by vision techniques and prior knowledge in a unified probabilistic graph model. Then the problem is stated as a pose parameters likelihood maximization estimation with the relations being the latent variables. Classification expectation maximization algorithm (CEM) is employed to solve the model. Besides, the contradiction between relations is incorporated as prior knowledge to better shape the posterior, thus guiding the algorithm to a better search direction. In experiments, both simulated and real world datasets are applied to evaluate the performance of our proposed method. The experimental results show that the AG gives better accuracy than the RDV (Relations as Deterministic Variables) employed in some previous works due to the robustness and global consistency of the AG. Finally, the solution is implemented into a PBD system with ABB industrial robotic arm simulator as the execution stage, succeeding in execution of real world captured assembly tasks.

 

 

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