BROWSE
Detail
Conference Paper
Knowledge-Based Reinforcement Learning for Industrial Robotic Assembly
Cited 1 time in 
Share 
Share
- Authors
- In Jun Park, Hyonyoung Han, Joonmyun Cho, Jun Hee Park
- Issue Date
- 2023-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1684-1689
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Although reinforcement learning has shown promise in solving industrial assembly tasks, it still faces challenges such as poor sample efficiency and sparse rewards that limit its learning capability. To address these challenges, we split multi-step assembly tasks into modular sub-tasks and use CAD-based prior knowledge to facilitate sub-task learning. The geometric information from the knowledge extraction module accelerates the learning of grasping and placing. Instead of considering multiple possible placement errors in a jig-free environment during training, which would significantly increase training time, our method uses a compensation module with a spatial transformer network to deal with errors. We evaluated our method on two 3D-printed models with different materials and achieved a completion success rate of 96% for the plastic model and 94% for the metal model. Our results indicate that our model can be robustly applied to products with similar geometry without requiring additional model updates.
- KSP Keywords
- 3D printed, Different materials, Geometric information, Industrial assembly, Knowledge extraction, Metal model, Multi-step, Reinforcement learning(RL), Robotic assembly, Spatial transformer network, Sub-tasks