借助好奇机器人进行符号接地的无监督、自底向上类别发现
Unsupervised, Bottom-up Category Discovery for Symbol Grounding with a Curious Robot
AI总结：
本文提出让具备好奇心模型的机器人通过视觉观察与动作自主进行自底向上的无监督类别发现，从而支持后续专家赋予符号并实现符号接地。
摘要：
为解决符号接地问题，并受儿童早期语言发展启发，我们利用了一个配备近似好奇心模型的机器人，重点关注自底向上地构建扎根于物理世界的无监督类别。也就是说，机器人并非从自顶向下的符号（例如指代某个物体的词语）出发并通过提供预定样本来赋予意义，而是自主、渐进地将其探索空间划分为一系列日益具体的无标签类别；在这一阶段，外部专家可以选择性地提供符号关联。我们通过使用一个能够观察视觉世界的机器人、引入更高维的感知空间，并采用更具泛化能力的类别构建方法，扩展了先前的工作。我们的实验表明，机器人会根据动作及其视觉观察到的内容学习类别，并且这些类别能够被符号化地接地。
英文摘要：
Towards addressing the Symbol Grounding Problem and motivated by early childhood language development, we leverage a robot which has been equipped with an approximate model of curiosity with particular focus on bottom-up building of unsupervised categories grounded in the physical world. That is, rather than starting with a top-down symbol (e.g., a word referring to an object) and providing meaning through the application of predetermined samples, the robot autonomously and gradually breaks up its exploration space into a series of increasingly specific unlabeled categories at which point an external expert may optionally provide a symbol association. We extend prior work by using a robot that can observe the visual world, introducing a higher dimensional sensory space, and using a more generalizable method of category building. Our experiments show that the robot learns categories based on actions and what it visually observes, and that those categories can be symbolically grounded into.https://info.arxiv.org/help/prep#comments
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。
