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

• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2934174

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.