Learning Ability Models for Human-Robot Collaboration (bibtex)

by Alexandra Kirsch and Fan Cheng

Abstract:

Our vision is a pro-active robot that assists elderly or disabled people in everyday activities. Such a robot needs knowledge in the form of prediction models about a person's abilities, preferences and expectations in order to decide on the best way to assist. We are interested in learning such models from observation. We report on a first approach to learn ability models for manipulation tasks and identify some general challenges for the acquisition of human models.

Reference:

Alexandra Kirsch and Fan Cheng, "Learning Ability Models for Human-Robot Collaboration", In Robotics: Science and Systems (RSS) — Workshop on Learning for Human-Robot Interaction Modeling, 2010.

Bibtex Entry:

@inproceedings{kirsch10learning,
  author    = {Alexandra Kirsch and Fan Cheng},
  title     = {Learning Ability Models for Human-Robot Collaboration},
  booktitle = {Robotics: Science and Systems (RSS) --- Workshop on Learning for Human-Robot Interaction Modeling},
  year      = {2010},
  bib2html_pubtype = {Workshop Paper},
  bib2html_groups  = {PARA},
  bib2html_funding = {CoTeSys},
  bib2html_rescat  = {Models, Human-Robot Interaction},
  bib2html_domain  = {Assistive Household},
  abstract = {Our vision is a pro-active robot that assists elderly or disabled people in everyday activities. Such a robot needs knowledge in the form of prediction models about a person's abilities, preferences and expectations in order to decide on the best way to assist. We are interested in learning such models from observation. We report on a first approach to learn ability models for manipulation tasks and identify some general challenges for the acquisition of human models.}}