Some of the threats in the dynamic environment include the unpredictability of the motion of objects and interferences to the robotic grasp. In such conditions the traditional supervised and reinforcement learning approaches are ill suited because they rely on a large amount of labelled data and a predefined reward signal. More specifically in this paper we introduce an important and promising framework known as self supervised learning (SSL) whose goal is to apply to the RGBD sensor and proprioceptive data from robot hands in order to allow robots to learn and improve their grasping strategies in real time. The invariant SSL framework overcomes the deficiencies of the fixed labelling by adapting the SSL system to changes in the objects behavior and improving performance in dynamic situations. The above proposed method was tested through various simulations and real world trials, with the series obtaining enhanced grasp success rates of 15% over other existing methods, especially under dynamic scenarios. Also, having tested for adaptation times, it was confirmed that the system could adapt faster, thus applicable for use in the real world, such as in industrial automation and service robotics. In future work, the proposed approach will be expanded to more complex tasks, such as multi object manipulation and functions in the context of cluttered environments, in order to apply the proposed methodology to a broader range of robotic tasks.

翻译：动态环境中的威胁包括物体运动的不确定性以及对机器人抓取的干扰。在此类条件下，传统的监督学习与强化学习方法存在局限，因其依赖于大量标注数据和预定义的奖励信号。本文具体提出一种重要且前景广阔的自监督学习框架，其目标是通过处理来自机器人手部的RGBD传感器与本体感知数据，使机器人能够实时学习并优化抓取策略。该不变式自监督学习框架通过自适应调整系统以应对物体行为变化，克服了固定标注的缺陷，从而提升了动态场景下的性能。所提方法经过多组仿真与真实环境实验验证，在动态场景下抓取成功率较现有方法平均提升15%。适应性时间测试表明，该系统具备更快的适应能力，适用于工业自动化与服务机器人等实际应用场景。未来工作将把该方法拓展至更复杂的任务，例如杂乱环境中的多物体操作，以扩大该框架在机器人任务中的适用范围。