Transformer-XL在机器人学习从示范中的长序列任务应用
Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration
AI总结：
本文提出利用Transformer-XL处理机器人学习从示范中的长序列任务，通过多模态传感器输入提升任务成功率和计算效率。
摘要：
本文提出了将Transformer-XL应用于机器人学习从示范（LfD）中长序列任务的创新方法。所提出的框架有效整合了多种传感器输入，包括RGB-D图像、激光雷达和触觉传感器，以构建全面的特征向量。通过利用Transformer-XL的先进能力，特别是其注意力机制和位置编码，我们的方法能够处理多模态感知数据的固有复杂性和长期依赖性。广泛的实证评估结果表明，与传统方法如长短期记忆（LSTM）网络和卷积神经网络（CNN）相比，该方法在任务成功率、准确性和计算效率方面均有显著提升。研究结果表明，基于Transformer-XL的框架不仅增强了机器人的感知和决策能力，还为未来机器人学习从示范的发展提供了坚实的基石。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
