ACT Algorithm Based on Long-Short Distance Attention Mechanism

Robot manipulation technology has always been a core area of research, and imitation learning algorithms are crucial for improving task accuracy and efficiency. The ACT algorithm proposed by Zhao et al. performs excellently in fine-grained tasks; however, when handling complex tasks with long time spans, its memory capability and ability to capture long-range temporal dependencies need improvement. Additionally, the increased task complexity leads to a surge in computational resource demands. This paper proposes an improved algorithm based on the ACT algorithm, incorporating a long-short distance attention mechanism. The aim is to enhance memory efficiency for long-duration tasks and improve the ability to capture long-range temporal dependencies, optimize the understanding and utilization of long-term dependencies, improve the overall performance of complex tasks, and reduce the consumption of computational resources such as CPU and GPU.