触觉扫视：基于强化学习的三维物体识别
Seeing by haptic glance: reinforcement learning-based 3D object Recognition
摘要：
人类能够通过目标对象与他/她的手指之间的有限数量的触觉接触来进行3D识别，而无需看到对象。这种能力在认知神经科学中被定义为“触觉扫视”。现有的大多数三维识别模型都是基于密集的三维数据开发的。尽管如此，在许多现实生活中的用例中，机器人被用来通过触觉探索收集3D数据，只能收集有限数量的3D点。因此，在这项研究中，我们专注于解决如何获得具有认知代表性的3D关键点的目标对象与机器人和对象之间的有限的相互作用的棘手问题。提出了一种新的基于强化学习的框架，其中触觉探索过程（代理迭代地预测机器人探索的下一个位置）与具有主动收集的3D点的客观3D识别同时优化。由于模型仅在3D对象被准确识别时才被奖励，因此它被驱动以找到对象的稀疏但有效的触觉感知3D表示。实验结果表明，我们提出的模型优于最先进的模型。
英文摘要：
Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object. This capability is defined as `haptic glance' in cognitive neuroscience. Most of the existing 3D recognition models were developed based on dense 3D data. Nonetheless, in many real-life use cases, where robots are used to collect 3D data by haptic exploration, only a limited number of 3D points could be collected. In this study, we thus focus on solving the intractable problem of how to obtain cognitively representative 3D key-points of a target object with limited interactions between the robot and the object. A novel reinforcement learning based framework is proposed, where the haptic exploration procedure (the agent iteratively predicts the next position for the robot to explore) is optimized simultaneously with the objective 3D recognition with actively collected 3D points. As the model is rewarded only when the 3D object is accurately recognized, it is driven to find the sparse yet efficient haptic-perceptual 3D representation of the object. Experimental results show that our proposed model outperforms the state of the art models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
