"What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI
"What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI
复制标题
“这是什么？”
DOI：
10.1145/3434074.3447160
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Gkatzia D
中科院分区：
文献类型：
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作者：
Gkatzia D
社交机器人的目标是让机器人在与人类伙伴进行面对面的互动时，能够表现出社交智能行为。面对面社交互动的一个重要方面包括有效识别他们的周围环境，环境和其中的对象，以便能够讨论，描述和提供指示，以帮助说话者和听者之间的持续合作。尽管人类可以仅通过一个例子就有效地向对话者学习视觉对象的感知基础单词含义，但教会机器人基础单词含义仍然是一项非常具有挑战性、昂贵且资源密集型的任务。在本文中，我们提出了一种新的框架，机器人概念收购的飞行，结合少杆学习与主动学习。在这个框架中，机器人通过与人类协作执行任务来学习新概念。我们比较了不同的学习策略，在基于任务的评估与人类参与者，我们发现，主动学习显着优于非主动学习的替代方案，是更可取的参与者，同时增加他们的信任在社会机器人的能力。
Social robotics aim to equip robots with the ability to exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social interaction includes the efficient recognition of their surroundings, the environment and the objects within it, so as to be able to discuss, describe and provide instructions to assist continuous collaboration between the speaker and the listener. Although humans can efficiently learn from their interlocutors to perceptually ground word meanings of visual objects from just a single example, teaching robots to ground word meanings remains a very challenging, expensive and resource-intensive task. In this paper, we present a novel framework for robot concept acquisition on the fly, by combining few-shot learning with active learning. In this framework, a robot learns new concepts through collaboratively performing tasks with humans. We compared different learning strategies in a task-based evaluation with human participants, and we found that active learning significantly outperforms a non-active learning alternative, and is more preferable by the participants while increasing their trust in the social robot's capabilities.
从有限的数据集中学习：对自然语言生成和人机交互的影响
DOI：
--
发表时间：
2018
期刊：
IEEE/ACM International Conference on Human-Robot Interaction
影响因子：
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作者：
Jekaterina Belakova;Dimitra Gkatzia
通讯作者：
Dimitra Gkatzia