Nichola Abdo
Learning Task Preferences of Users
As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, different users typically have their own preferences, for example with respect to arranging objects on different shelves. As many of these preferences depend on a variety of factors including personal taste, cultural background, or common sense, it is challenging, even for an expert, to formulate or program a proper model to be used by a robot. At the same time, it is impractical for robots to constantly query users about how
they should perform individual tasks. In this work, we present an approach to learn patterns in user preferences for the task of tidying up objects in containers, e.g., shelves or boxes. Our method builds upon the paradigm of collaborative filtering for making personalized recommendations and relies on data from different users that we gather using crowdsourcing. To deal with novel objects for which we have no data, we propose a method that compliments standard collaborative filtering by leveraging information mined from the Web. When solving a tidy up task, we first predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsourcing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.
Relevant Manuscripts:
Nichola Abdo, Cyrill Stachniss, Luciano Spinello, Wolfram Burgard
Organizing Objects by Predicting User Preferences Through Collaborative Filtering
Accepted for publication in the International Journal of Robotics Research (IJRR)
[Preprint] [Video1] [Video2]
Code and data coming soon!
Nichola Abdo, Cyrill Stachniss, Luciano Spinello, Wolfram Burgard
Robot, Organize my Shelves! Tidying up Objects by Predicting User Preferences
IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, 2015.
Best service robotics paper award finalist.
[PDF] [Video]
Learning Task Preferences of Users
As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, different users typically have their own preferences, for example with respect to arranging objects on different shelves. As many of these preferences depend on a variety of factors including personal taste, cultural background, or common sense, it is challenging, even for an expert, to formulate or program a proper model to be used by a robot. At the same time, it is impractical for robots to constantly query users about how they should perform individual tasks. In this work, we present an approach to learn patterns in user preferences for the task of tidying up objects in containers, e.g., shelves or boxes. Our method builds upon the paradigm of collaborative filtering for making personalized recommendations and relies on data from different users that we gather using crowdsourcing. To deal with novel objects for which we have no data, we propose a method that compliments standard collaborative filtering by leveraging information mined from the Web. When solving a tidy up task, we first predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsourcing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.
Relevant Manuscripts:
Nichola Abdo, Cyrill Stachniss, Luciano Spinello, Wolfram Burgard
Organizing Objects by Predicting User Preferences Through Collaborative Filtering
Accepted for publication in the International Journal of Robotics Research (IJRR)
[Preprint] [Video1] [Video2]
Code and data coming soon!
Nichola Abdo, Cyrill Stachniss, Luciano Spinello, Wolfram Burgard
Robot, Organize my Shelves! Tidying up Objects by Predicting User Preferences
IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, 2015.
Best service robotics paper award finalist.
[PDF] [Video]