We present an algorithmic framework for the early classification of human intentions, and use it to accurately predict future human motions when planning the path of a robot in an environment that is shared with humans. During an off-line learning phase, a classifier that can recognize when a human intends to interact with the robot is trained. At runtime, this trained classifier allows us to recognize humans who intend to interact with, or obstruct, the robot in some way. We validate our approach using both recorded and simulated data in an environment in which some humans intentionally obstruct the robot. Our classifier identifies these potential blockers, thus allowing the robot to safely and efficiently navigate the environment by minimizing the chances of being blocked.
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