Wang, T., Zhang, H., Toh, W. Q., Zhu, H., Tan, C., Wu, Y., Liu, Y., Jing, W. (2019). Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 148–153. https://doi.org/10.1109/robio49542.2019.8961391
Abstract:
Learning-based methods have been used to program 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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering (AME) Programmatic Fund
Grant Reference no. : A18A2b0046