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

arXiv: https://arxiv.org/abs/2009.02041 · HTML: https://mlsanity.com/paper/2009.02041/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [HERO: Human Reaction Generation from Videos](https://mlsanity.com/paper/2503.08270/) (2025)
- [BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind](https://mlsanity.com/paper/2402.07402/) (2024)
- [Are Human Conversations Special? A Large Language Model Perspective](https://mlsanity.com/paper/2403.05045/) (2024)
- [HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario](https://mlsanity.com/paper/2509.10096/) (2025)
- [ReactMotion: Generating Reactive Listener Motions from Speaker Utterance](https://mlsanity.com/paper/2603.15083/) (2026)
- [A General, Evolution-Inspired Reward Function for Social Robotics](https://mlsanity.com/paper/2202.00617/) (2022)
- [OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models](https://mlsanity.com/paper/2501.00432/) (2024)
- [What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics](https://mlsanity.com/paper/2510.16435/) (2025)
- [Decoding Psychological States Through Movement: Inferring Human Kinesic Functions with Application to Built Environments](https://mlsanity.com/paper/2601.17194/) (2026)

## Abstract (arXiv)

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.