Scholarlink

<p>In the field of robotics control, prevailing research is progressively leveraging more sophisticated deep learning networks to enhance learning outcomes in specific domains of robotics motion control. Notably, the Decision Transformer (DT), a prominent model in motion control learning, predominantly focuses on long-term expected returns and often overlooks the critical significance of immediate action values, which are essential for the adaptive behaviors observed in higher animals. Addressing this gap, this paper introduces the Focused Reward Transformer (FRT) . This innovative training network combines guided attention mechanisms with a significant emphasis on reward processing. The FRT aims to optimize robot motion control by dynamically balancing long-term predictions with immediate environmental cues. The effectiveness of the FRT was rigorously evaluated by detailed simulations on the MuJoCo platform, using three different tasks: Hopper, Walker2d, and Halfcheetah. These tasks broadly represent the challenges inherent to robotics dynamics. Our experimental results demonstrate a substantial improvement in the performance of control tasks, the ability to validate FRT, refine robotic control, and skillfully combine in-time context responses with the long-term planning of strategies. This confirms the potential of FRT to advance the field of robotic control by introducing more nuanced approaches to learning and decision-making processes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Robot motion skill learning method based on focused reward transformer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月13日

查看全文
  • Borui Nan,
  • Xinyi Zheng,
  • Wanru Gong,
  • Lanyue Bi,
  • Xiaoyv Zhu,
  • Xiaoqing Zhu

摘要

In the field of robotics control, prevailing research is progressively leveraging more sophisticated deep learning networks to enhance learning outcomes in specific domains of robotics motion control. Notably, the Decision Transformer (DT), a prominent model in motion control learning, predominantly focuses on long-term expected returns and often overlooks the critical significance of immediate action values, which are essential for the adaptive behaviors observed in higher animals. Addressing this gap, this paper introduces the Focused Reward Transformer (FRT) . This innovative training network combines guided attention mechanisms with a significant emphasis on reward processing. The FRT aims to optimize robot motion control by dynamically balancing long-term predictions with immediate environmental cues. The effectiveness of the FRT was rigorously evaluated by detailed simulations on the MuJoCo platform, using three different tasks: Hopper, Walker2d, and Halfcheetah. These tasks broadly represent the challenges inherent to robotics dynamics. Our experimental results demonstrate a substantial improvement in the performance of control tasks, the ability to validate FRT, refine robotic control, and skillfully combine in-time context responses with the long-term planning of strategies. This confirms the potential of FRT to advance the field of robotic control by introducing more nuanced approaches to learning and decision-making processes.