Adapting object handover from robot to human using perceptual affordances

This describes a method for a robot to intelligently hand over an object to a human. The robot uses sensor data to understand its environment, the object it's holding, and human actions. It then calculates the best way to perform the handover based on what it perceives and a learned preference model, improving its technique over time through feedback.

Why it matters: Filed before the widespread adoption of advanced deep learning and reinforcement learning. These AI advancements could now significantly enhance the accuracy of perceptual classification and the efficiency of learning the preference model, making the robot's handover more intuitive and adaptable.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedRobot hardware, sensor integration, complex software system

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