ICRA 2011 Paper Abstract


Paper TuP101.5

Tipaldi, Gian Diego (University of Freiburg), Arras, Kai Oliver (University of Freiburg)

I Want My Coffee Hot! Learning to Find People under Spatio-Temporal Constraints

Scheduled for presentation during the Regular Sessions "Personal and Service Robots" (TuP101), Tuesday, May 10, 2011, 14:40−14:55, Room 3B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Robot Companions and Social Human-Robot Interaction, Domestic Robots, Physical Human-Robot Interaction


In this paper we present a probabilistic model for spatio-temporal patterns of human activities that enables robots to blend themselves into the workflows and daily routines of people. The model, called spatial affordance map, is a non-homogeneous spatial Poisson process that relates space, time and occurrence probability of activity events. We describe how learning and inference is made and present a novel planning algorithm that produces paths which maximize the probability to encounter a person. We show that the problem is a special class of the orienteering problem that can be solved as a finite horizon Markov decision process.

We develop a simulator of populated office environments to validate the model and the planning algorithm. The simulated agents follow activity patterns learned by administering a questionnaire to 27 colleagues over two weeks. The experiments shows that the model is statistically valid with respect to both the Anderson-Darling test and the expected waiting time estimation. They further show that the proposed algorithm is able to find optimal paths.



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