CMRM: A Cross-Modal Reasoning Model to Enable Zero-Shot Imitation Learning for Robotic RFID Inventory in Unstructured Environments
CMRM: A Cross-Modal Reasoning Model to Enable Zero-Shot Imitation Learning for Robotic RFID Inventory in Unstructured Environments
复制标题
DOI：
10.1109/globecom54140.2023.10437833
复制
发表时间：
2023-12
期刊：
影响因子：
--
通讯作者：
Yongshuai Wu;Jian Zhang;Shaoen Wu;Shiwen Mao;Ying Wang
中科院分区：
文献类型：
--
作者：
Yongshuai Wu;Jian Zhang;Shaoen Wu;Shiwen Mao;Ying Wang
深度学习技术的快速发展使其成为各种自主机器人系统的一种很有前途的技术。最近，研究人员探索部署深度学习模型，如强化学习和模仿学习，使机器人能够完成基于射频识别（RFID）的库存任务。然而，现有的方法要么专注于单一领域，要么需要大量的数据和时间来训练。为了解决这些问题，本文提出了一种跨模态推理模型（CMRM），该模型旨在从多个传感器中提取高维信息，并学习从空间和历史特征中推断潜在的跨模态关系。此外，CMRM将学习到的任务策略与高级特征对齐，以提供对未知环境的零射击泛化。我们在几个虚拟环境以及室内环境中进行了广泛的实验，并使用机器人进行RFID库存。实验结果表明，所提出的CMRM可以将学习效率显著提高20倍左右。它还演示了在不可见的环境中部署学习策略以成功执行RFID库存任务的稳健零概率泛化。
The fast development in Deep Learning (DL) has made it a promising technique for various autonomous robotic systems. Recently, researchers have explored deploying DL models, such as Reinforcement Learning and Imitation Learning, to enable robots for Radio-frequency Identification (RFID) based inventory tasks. However, the existing methods are either focused on a single field or need tremendous data and time to train. To address these problems, this paper presents a Cross-Modal Reasoning Model (CMRM), which is designed to extract high-dimension information from multiple sensors and learn to reason from spatial and historical features for latent cross-modal relations. Furthermore, CMRM aligns the learned tasking policy to high-level features to offer zero-shot generalization to unseen environments. We conduct extensive experiments in several virtual environments as well as in indoor settings with robots for RFID inventory. The experimental results demonstrate that the proposed CMRM can significantly improve learning efficiency by around 20 times. It also demonstrates a robust zero-shot generalization for deploying a learned policy in unseen environments to perform RFID inventory tasks successfully.