This paper describes a modeling method for predicting a human's task-level intent through the use of Markov Decision Processes. Intent prediction can be used by a robot to improve decision-making when human and robot operate in a shared physical space. This work presumes human and robot goals are independent such that the robot seeks to avoid interfering with the human rather than directly assisting the human. The proposed human intent prediction system transforms goal sequences the human is expected to complete, a limited past action history, and a correlation of observed behaviors with actions into a prediction of the in-progress or next action the humans is most likely to take. An intra-vehicle activity space robotics application example is presented.

No abstract

This paper describes a modeling method for predicting a human's task-level intent through the use of Markov Decision Processes. Intent prediction can be used by a robot to improve decision-making when human and robot operate in a shared physical space. This work presumes human and robot goals are independent such that the robot seeks to avoid interfering with the human rather than directly assisting the human. The proposed human intent prediction system transforms goal sequences the human is expected to complete, a limited past action history, and a correlation of observed behaviors with actions into a prediction of the in-progress or next action the humans is most likely to take. An intra-vehicle activity space robotics application example is presented.

No abstract

No abstract