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Summary: I want my coffee hot! Learning to find people
under spatio-temporal constraints
Gian Diego Tipaldi Kai O. Arras
Abstract--In this paper we present a probabilistic
model for spatio-temporal patterns of human activities
that enable robots to blend themselves into the work-
flows 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 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-
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