论文标题
通过预处理对物体的粗糙负担能力图进行加速学习
Accelerating Grasp Learning via Pretraining with Coarse Affordance Maps of Objects
论文作者
论文摘要
自我监督的学习学习，即通过反复试验学习掌握，取得了长足的进步。但是，训练这样的模型仍然很耗时，在实践中应用它仍然是一个挑战。这项工作提出了一种加速的机器人掌握方法，该方法是通过基于相当小的数据集对物体的粗糙负担能力图进行预处理的。通过预训练产生的模型是一种初始化政策，以热情地开始学习，以指导机器人在培训开始时捕获更有效的奖励。用单个关键点注释其粗糙的负担图中的一个物体，从而大大减轻了标签的负担。进行了模拟和实际机器人的广泛实验，以评估所提出的方法。模拟结果表明，在基于Q -Network的方法的方法上，它可以显着加速学习近三倍。它对真正的UR3机器人的测试表明，它仅在大约两个小时内掌握了500次的掌握尝试，达到了89.5％的掌握率，这比竞争对手快四倍。此外，它具有出色的概括能力，可以掌握先前的新颖对象。它的表现优于某些现有方法，并且有可能直接应用于机器人进行现实世界的学习任务。
Self-supervised grasp learning, i.e., learning to grasp by trial and error, has made great progress. However, it is still time-consuming to train such a model and also a challenge to apply it in practice. This work presents an accelerating method of robotic grasp learning via pretraining with coarse affordance maps of objects to be grasped based on a quite small dataset. A model generated through pre-training is harnessed as an initialization policy to warmly start grasp learning so as to guide a robot to capture more effective rewards at the beginning of training. An object in its coarse affordance map is annotated with a single key point and thereby, the burden of labeling is greatly alleviated. Extensive experiments in simulation and on a real robot are conducted to evaluate the proposed method. The simulation results show that it can significantly accelerate grasp learning by nearly three times over a vanilla Deep Q-Network -based method. Its test on a real UR3 robot shows that it reaches a grasp success rate of 89.5% via only 500 times of grasp tries within about two hours, which is four times faster than its competitor. In addition, it enjoys an outstanding generalization ability to grasp prior-unseen novel objects. It outperforms some existing methods and has the potential to directly apply to a robot for real-world grasp learning tasks.
