Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration

TL;DR

This paper introduces a Transformer-XL based framework for robotic learning from demonstrations, effectively handling long multi-modal sequences to improve perception, decision-making, and task success rates.

Contribution

It presents a novel application of Transformer-XL in robotic LfD, integrating multi-modal sensory data for better long-term dependency modeling.

Findings

Significant improvements in task success rates and accuracy.

Enhanced computational efficiency over LSTM and CNN methods.

Robust perception and decision-making in robotic tasks.

Abstract

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.