University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Using spatio-temporal knowledge for activity recognition and monitoring in Human-Robot Interaction

Using spatio-temporal knowledge for activity recognition and monitoring in Human-Robot Interaction

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If you have a question about this talk, please contact Leandro Minku.

Host: Dr. Nick Hawes

Personal robots that are useful helpers for humans need to know about common tasks and activities of their human partners to be able to react adequately to human behavior. Thus, knowledge about human task performance becomes an inevitable part of a robotic system that is aimed to work together with humans in human centered environments like a household.

In this talk I will show an approach that enables robots to acquire general, spatio-temporal plan representations (STPRs) from human motion tracking data in different environments given a semantically annotated map of the environment. Those models can successfully be used to create hierarchical Hidden Markov Models (HHMMs) and perform activity recognition and monitoring with inexpensive depth cameras in spatially limited environments. This enables a robot to maintain a probabilistic belief-state module about human task performance to react to human actions and even anticipate possible next actions of its human partner. The integration of this belief-state module into human-aware planning can enable a robot to better adapt its behavior to its human partner, thus being a more efficient helper for the human.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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