Robots predict human intention for faster builds

Science  Daily April 5, 2023
To focus on enabling robots to proactively assist humans in assembly tasks by adapting to their preferred sequence of actions researchers at the University of Southern California proposed learning human preferences from demonstrations in a shorter, canonical task to predict user actions in the actual assembly task. The proposed system used the preference model learned from the canonical task as a prior and updates the model through interaction when predictions are inaccurate. They evaluated the proposed system in simulated assembly tasks and in a real-world human-robot assembly study and showed that both transferring the preference model from the canonical task, as well as updating the model online, contributed to improved accuracy in human action prediction. According to the researchers this enables the robot to proactively assist users, significantly reduce their idle time, and improve their experience working with the robot, compared to a reactive robot… read more. Open Access TECHNICAL ARTICLE.