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 […]