.png){kind=icon}
返回
Evaluating Task Optimization and Reinforcement Learning Models in Robotic Task Parameterization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3504354.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The rapid evolution of industrial robot hardware has created a technological gap with software, limiting its adoption. The software solutions proposed in recent years have yet to meet the industrial sector's requirements, as they focus more on the definition of task structure than the definition and tuning of its execution parameters. A framework for task parameter optimization was developed to address this gap. It breaks down the task using a modular structure, allowing the task optimization piece by piece. The optimization is performed with a dedicated hill-climbing algorithm. This paper revisits the framework by proposing an alternative approach that replaces the algorithmic component with reinforcement learning (RL) models. Five RL models are proposed with increasing complexity and efficiency. A comparative analysis of the traditional algorithm and RL models is presented, highlighting efficiency, flexibility, and usability. The results demonstrate that although RL models improve task optimization efficiency by 95%, they still need more flexibility. However, the nature of these models provides significant opportunities for future advancements.
Keyword:
Robots
Optimization
Programming
Reinforcement learning
Service robots
Artificial intelligence
Robot sensing systems
Software
Mathematical models
Libraries
robotic task optimization
task-oriented programming
intuitive robot programming
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Embodied intelligence in manufacturing: leveraging large language models for autonomous industrial robotics制造业中的具体化智能: 利用大型语言模型实现自主工业机器人
PREAI
分享收藏Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects使用卷积神经网络工具进行人类活动识别: 最新技术综述，数据集，挑战和未来前景
OAAI
OAAI分享收藏
查看更多内容