Mahmoudi Siavash, Davar Amirreza, Sohrabipour Pouya, Bist Ramesh Bahadur, Tao Yang, Wang Dongyi
Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR, United States.
Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR, United States.
Front Robot AI. 2024 Oct 17;11:1441312. doi: 10.3389/frobt.2024.1441312. eCollection 2024.
Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models and frameworks, and a thorough assessment of performance metrics employed in the surveyed studies. Additionally, it includes a comparative analysis between imitation learning techniques and conventional control methodologies in the realm of robotics. The findings derived from this survey unveil profound insights into the applications of imitation learning in agricultural robotics. These methods are highlighted for their potential to significantly improve task execution in dynamic and high-dimensional action spaces prevalent in agricultural settings, such as precision farming. Despite promising advancements, the survey discusses considerable challenges in data quality, environmental variability, and computational constraints that IL must overcome. The survey also addresses the ethical and social implications of implementing such technologies, emphasizing the need for robust policy frameworks to manage the societal impacts of automation. These findings hold substantial implications, showcasing the potential of imitation learning to revolutionize processes in agricultural robotics. This research significantly contributes to envisioning innovative applications and tools within the agricultural robotics domain, promising heightened productivity and efficiency in robotic agricultural systems. It underscores the potential for remarkable enhancements in various agricultural processes, signaling a transformative trajectory for the sector, particularly in the realm of robotics and autonomous systems.
模仿学习（IL）作为机器学习中一个新兴的前沿领域，在各个领域都有着巨大的潜力。近年来，它在机器人技术中的应用引发了广泛关注，为自主控制过程带来了显著进展。本文对农业机器人技术中模仿学习技术的应用进行了全面深入的探讨。该综述严格考察了利用模仿学习来应对关键农业挑战的各种研究工作。在方法上，本综述全面研究了模仿学习在农业机器人技术应用中的多方面情况。该综述涵盖了确定可以通过模仿学习潜在解决的农业任务、对特定模型和框架的详细分析，以及对所调查研究中使用的性能指标的全面评估。此外，还包括在机器人技术领域中模仿学习技术与传统控制方法之间的比较分析。本次综述得出的结果揭示了模仿学习在农业机器人技术中的应用的深刻见解。这些方法因其有潜力显著改善农业环境中普遍存在的动态和高维动作空间（如精准农业）中的任务执行而受到关注。尽管取得了有前景的进展，但该综述讨论了模仿学习必须克服的数据质量、环境可变性和计算约束等重大挑战。该综述还探讨了实施此类技术的伦理和社会影响，强调需要强大的政策框架来管理自动化对社会的影响。这些发现具有重大意义，展示了模仿学习变革农业机器人技术流程的潜力。这项研究对设想农业机器人技术领域的创新应用和工具做出了重大贡献，有望提高机器人农业系统的生产力和效率。它强调了在各种农业过程中实现显著改进的潜力，标志着该领域，特别是在机器人技术和自主系统领域的变革性发展轨迹。
