This article presents a comprehensive survey of the integration of machine learning techniques into robotic grasping, with a special emphasis on the challenges and advancements for space applications. The incorporation of artificial intelligence, particularly through deep learning, reinforcement learning, transfer learning, convolutional neural networks and recurrent neural networks, has significantly revolutionized robotic grasping. These advancements facilitate autonomous, efficient, and sophisticated manipulation in the challenging environment of outer space, transitioning from traditional mechanical grippers to sophisticated systems powered by advanced algorithms. This transition highlights the critical integration of sensory perception, grasp planning, and execution mechanisms, enhancing robots' capabilities to perceive, interact with, and manipulate objects with unprecedented precision and adaptability. The article meticulously outlines significant advancements achieved through the deployment of convolutional neural networks for visual information processing, RNNs for sequential decision-making, RL for autonomous strategy refinement, and transfer learning for leveraging pre-learned knowledge in novel tasks. These technologies address the unique challenges of space environments, such as varied textures, occlusions, microgravity conditions, and the sim-to-real gap, by enhancing sample efficiency, improving sim-to-real transfer capabilities, and integrating multimodal data for better object localization and pose estimation. Furthermore, the review explores the specific challenges faced in space robotic grasping, including handling varied textures and occlusions, adapting to unpredictable conditions, achieving real-time processing, and ensuring safety and reliability. It proposes future research directions focused on overcoming these hurdles, such as enhanced generalization through multimodal learning, robust sim-to-real transfer techniques, and the development of collaborative robotics and swarm intelligence. Critical to the development of ML models for robotic grasping are the roles of specialized datasets and simulation environments. Datasets like the Cornell Grasping Dataset and the Yale-CMU-Berkeley Object, along with simulation platforms such as Gazebo and PyBullet, provide essential resources for training, testing, and refining ML models. These tools enable researchers to simulate complex robotic systems and interactions within realistic environments, fostering rapid iterations on design and control strategies. In summary, this article offers in-depth insights into the progress, current challenges, and future prospects of machine learning techniques in robotic grasping for space exploration. It showcases significant strides made in the field and charts a path forward, emphasizing the need for innovative solutions to navigate the complexities of robotic manipulation in outer space. Through the strategic integration of advanced ML techniques, the development of adaptable and efficient robotic systems for space applications continues to advance, promising to unlock new possibilities in space exploration and beyond.

中文翻译：

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空间应用中机器人抓取控制机器学习综述

本文对机器学习技术与机器人抓取的集成进行了全面的调查，特别强调了空间应用的挑战和进步。人工智能的结合，特别是通过深度学习、强化学习、迁移学习、卷积神经网络和循环神经网络，极大地改变了机器人抓取。这些进步有助于在充满挑战的外太空环境中实现自主、高效和复杂的操纵，从传统的机械夹具过渡到由先进算法驱动的复杂系统。这一转变凸显了感官感知、抓取规划和执行机制的关键整合，增强了机器人以前所未有的精度和适应性感知、交互和操纵物体的能力。本文详细概述了通过部署用于视觉信息处理的卷积神经网络、用于顺序决策的 RNN、用于自主策略细化的 RL 以及用于在新任务中利用预先学习的知识的迁移学习所取得的重大进步。这些技术通过提高采样效率、改善模拟到真实的传输能力以及集成多模态数据以获得更好的物体，解决了空间环境的独特挑战，例如不同的纹理、遮挡、微重力条件和模拟到真实的差距。定位和姿态估计。此外，该评论还探讨了太空机器人抓取面临的具体挑战，包括处理不同的纹理和遮挡、适应不可预测的条件、实现实时处理以及确保安全性和可靠性。它提出了未来的研究方向，重点是克服这些障碍，例如通过多模式学习增强泛化、强大的模拟到真实的传输技术以及协作机器人和群体智能的开发。专门数据集和模拟环境的作用对于机器人抓取的机器学习模型的开发至关重要。 Cornell Grasping Dataset 和 Yale-CMU-Berkeley Object 等数据集，以及 Gazebo 和 PyBullet 等模拟平台，为训练、测试和完善 ML 模型提供了必要的资源。这些工具使研究人员能够模拟复杂的机器人系统和现实环境中的交互，促进设计和控制策略的快速迭代。总之，本文深入探讨了太空探索机器人抓取中机器学习技术的进展、当前挑战和未来前景。它展示了该领域取得的重大进展并描绘了前进的道路，强调需要创新的解决方案来应对外太空机器人操纵的复杂性。通过先进机器学习技术的战略整合，用于太空应用的适应性强、高效的机器人系统的开发不断取得进展，有望在太空探索及其他领域释放新的可能性。