One of the fundamental modes of learning in children is through curiosity. Children (and adults) interact with new people, learn about novel objects, activities and other stimuli through curiosity and other intrinsic motivations. Creating autonomous robots that learn continually through intrinsic curiosity may result in breakthroughs in artificial intelligence. Such robots could continue to learn about themselves and the world around them through curiosity, thus improving their abilities over their 'lifetime'. Although recent works on curiosity in different fields have produced significant results, most of these works have focused on constrained simulated environments which do not involve human interaction. However, in real-world applications such as healthcare, home-assistance etc., robots generally have to interact with humans on a regular basis. In these scenarios, it is imperative that curiosity is directed towards seeking out and learning important information from the humans when needed rather than simply learning in an unsupervised manner. Further, there is limited work on how humans perceive such curious robots and whether humans prefer curious robots that adapt over time to other robots that simply perform their assigned tasks. In this workshop, our goal is to bring together researchers and practitioners in different multidisciplinary fields to discuss the role of robot curiosity in real-world applications and its implications in human-robot interaction (HRI).
|Number of pages||2|
|Publication status||Published - 7 Mar 2022|
|Event||17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) - Sapporo, Japan|
Duration: 7 Mar 2022 → 10 Mar 2022
Conference number: 17
|Conference||17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)|
|Period||7/03/22 → 10/03/22|