Robot motion skill learning method based on focused reward transformer.

ABSTRACT: 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 en

SUBMITTER: Nan B 

PROVIDER: S-EPMC12615603 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature