Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Automatic assembly of elastic components is difficult because of the potential deformation of parts during the assembly process. Consequently, robots cannot adapt their manipulation to dynamic changes. Designing systems that learn assembly skills can help in alleviating the uncertain factor for indu...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.6282-6294
Main Authors: Li, Fengming, Jiang, Qi, Quan, Wei, Cai, Shibo, Song, Rui, Li, Yibin
Format: Article
Language:English
Subjects:
acquisition of manipulation skills
Algorithms
Assembly
deep reinforcement learning
Elastic deformation
Industrial robots
Machine learning
multi-modal information description
Parameters
Reinforcement learning
Robot kinematics
Robotic assembly
Robots
Searching
Service robots
Skills
Strain
Target recognition
Task analysis
Two dimensional analysis
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Summary:Automatic assembly of elastic components is difficult because of the potential deformation of parts during the assembly process. Consequently, robots cannot adapt their manipulation to dynamic changes. Designing systems that learn assembly skills can help in alleviating the uncertain factor for industrial-grade assembly robots. This study proposes a skill acquisition method based on multi-modal information description to realize the assembly of systems with elastic components. This multi-modal information includes two-dimensional images, poses, forces/torques, and robot joint parameters. In this method, robots acquire searching, location determination, and pose adjustment skills using these multi-modal information parameters. As a result, robots can reach the assembly target by analyzing two-dimensional images with no position constraint. While acquiring pose adjustment skills, the reward function with depth and assembly steps is used to improve the learning efficiency. The deep deterministic policy gradient (DDPG) algorithm is applied for acquiring skills. Experiments using a KUKA iiwa robot demonstrated the effectiveness and conciseness of our method. Our results indicate that the robot acquired searching, location determination, and pose adjustment skills that allowed it to successfully complete elastic assembly.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2934174