---
title: "Learning Social Affordance for Human-Robot Interaction"
authors:
  - "Tianmin Shu"
  - "Michael S. Ryoo"
  - "Song-Chun Zhu"
doi: ""
url: "https://ijcai.org/proceedings/2016/488"
pdf: "https://ijcai.org/proceedings/2016/0488.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3454-3460"
track: ""
keywords:
---

# Learning Social Affordance for Human-Robot Interaction

Tianmin Shu, Michael S. Ryoo, Song-Chun Zhu

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3454-3460.

## Abstract

In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to complete each sub-event (i.e., sub-goal). This enables the robot to infer its own movement in reaction to the human body motion, allowing it to naturally replicate such interactions.