  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
two 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 cameras. 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.
