ML Anthology

Learning Social Affordance for Human-Robot Interaction

Tianmin Shu, Michael S. Ryoo, Song-Chun Zhu
IJCAI 2016 pp. 3454-3461
/ijcai/2016/shu2016ijcai-learning/

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. PDF

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Cite

Text

Shu et al. "Learning Social Affordance for Human-Robot Interaction." International Joint Conference on Artificial Intelligence, 2016.

Markdown

[Shu et al. "Learning Social Affordance for Human-Robot Interaction." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/shu2016ijcai-learning/)

BibTeX

@inproceedings{shu2016ijcai-learning,
  title     = {{Learning Social Affordance for Human-Robot Interaction}},
  author    = {Shu, Tianmin and Ryoo, Michael S. and Zhu, Song-Chun},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {3454-3461},
  url       = {https://mlanthology.org/ijcai/2016/shu2016ijcai-learning/}
}