AIR-Act2Act: Human–human interaction dataset for teaching non-verbal social behaviors to robots

To better interact with users, a social robot should understand the users’ behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly tellin...

Full description

Saved in:
Bibliographic Details
Published in:The International journal of robotics research 2021-04, Vol.40 (4-5), p.691-697
Main Authors: Ko, Woo-Ri, Jang, Minsu, Lee, Jaeyeon, Kim, Jaehong
Format: Article
Language:English
Subjects:
Algorithms
Automation
Behavior
Colleges & universities
Datasets
Domains
Human behavior
Human relations
Humanoid
Indoor environments
Machine learning
Older people
Programming languages
Robots
Service robots
Skills
Social skills
Teaching
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To better interact with users, a social robot should understand the users’ behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human–human interaction videos. However, human–human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human–human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and 2 college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes, and 3D skeletal data that are captured with three Microsoft Kinect v2 sensors. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful Python scripts are available for download at https://github.com/ai4r/AIR-Act2Act. It can be used to not only teach social skills to robots but also benchmark action recognition algorithms.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364921990671