2019/12/11 by Tianying Wang, Wang, Tianying, Hao Zhang +13
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.1912.05205
Accepted by ROBIO 2019
arxiv created 2019/12/11 · openalex publication_date 2019/12/11 · arxiv updated 2019/12/12 · openalex created_date 2019/12/26 · openalex updated_date 2026/08/01
Learning-based methods have been used to pro-gram robotic tasks in recent years. However, extensive training is usually required not only for the initial task learning but also for generalizing the learned model to the same task but in different environments. In this paper, we propose a novel Deep Reinforcement Learning algorithm for efficient task generalization and environment adaptation in the robotic task learning problem. The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem. The proposed Deep Model Fusion (DMF) method reuses and combines the previously trained model to improve the learning efficiency and results.Besides, we also introduce a Multi-objective Guided Reward(MGR) shaping technique to further improve training efficiency.The proposed method was benchmarked with previous methods in various environments to validate its effectiveness.