  This paper presents an innovative application of Transformer-XL for long
sequence tasks in robotic learning from demonstrations (LfD). The proposed
framework effectively integrates multi-modal sensor inputs, including RGB-D
images, LiDAR, and tactile sensors, to construct a comprehensive feature
vector. By leveraging the advanced capabilities of Transformer-XL, particularly
its attention mechanism and position encoding, our approach can handle the
inherent complexities and long-term dependencies of multi-modal sensory data.
The results of an extensive empirical evaluation demonstrate significant
improvements in task success rates, accuracy, and computational efficiency
compared to conventional methods such as Long Short-Term Memory (LSTM) networks
and Convolutional Neural Networks (CNNs). The findings indicate that the
Transformer-XL-based framework not only enhances the robot's perception and
decision-making abilities but also provides a robust foundation for future
advancements in robotic learning from demonstrations.
