# Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning

arXiv: https://arxiv.org/abs/1912.05205 · HTML: https://mlsanity.com/paper/1912.05205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration](https://mlsanity.com/paper/2110.14817/) (2021)
- [End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation](https://mlsanity.com/paper/2202.06027/) (2022)
- [Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision](https://mlsanity.com/paper/2608.17628/) (2026)

## Abstract (arXiv)

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.