Improving Robot Response to Anticipate Human Actions-IEEE

Researchers at Cornell University have created a machine-learning model for generating an appropriate robot response based on evaluating human activities. For these researchers, the secret to creating a technological "glass ball" for robots to anticipate our actions is a conditional random field (CRF) model and Kinect real-time technology typically used in video games to trace motions. Capabilities such as these will open this technology up to a range of applications spanning from the restaurant industry to manufacturing lines. The idea of creating a model that allows robots to consistently and successfully respond to our actions stemmed from the notion that robots unable to anticipate and react to humans could be viewed as impractical in human-robot interactions. As machine hardware and software continues to advance, this model could be the next critical step for preparing robots to better integrate with natural human behavior. Previous research has been successful in enabling a robot to see human activities and label them, but have not found success in using that labeling system to anticipate the future.