Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912760438194176 |
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| author | Tianci, Gao |
| author_facet | Tianci, Gao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15562 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration Tianci, Gao Robotics 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. |
| title | Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.15562 |