I want my co ee hot! Learning to nd people under spatio-temporal constraints

摘要：

In this paper we present a probabilistic model for spatio-temporal patterns of human activities that enable robots to blend themselves into the work- ows and daily routines of people. The model, called spatial aordance 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 nite horizon Markov decision process. We develop a simulator of populated oce environ- ments to validate the model and the planning algo- rithm. The simulated agents follow activity patterns learned by administering a questionnaire to 27 col- leagues 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 nd optimal paths.

作者: Gian Diego Tipaldi Kai O. Arras

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 1217-1222

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）