用自监督学习让机器人在动态环境中实时提升抓取成功率。
Self-Supervised Learning For Robust Robotic Grasping In Dynamic Environment
- 利用RGBD与本体感知数据，构建自监督框架
- 动态场景下抓取成功率提升15%，适应更快
- 适合工业自动化与服务机器人等实时场景
动态环境中物体运动不可预测且存在干扰，传统监督与强化学习因依赖大量标注数据和预设奖励信号而效果不佳。本文提出一种自监督学习（SSL）框架，基于机器人手部的RGBD与本体感知数据，使机器人能实时学习并优化抓取策略。该框架通过自适应机制应对物体行为变化，克服固定标签的局限，在多种仿真与真实实验中表现出色，动态场景下抓取成功率较现有方法提升15%。同时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。未来工作将拓展至多物体操作及杂乱环境下的复杂任务。
原文摘要 · Abstract (English)
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.
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