Skill Generalization with Verbs
This addresses the challenge of robot-human interaction through natural language commands, but it is incremental as it builds on existing methods for skill generalization.
The paper tackles the problem of enabling robots to understand natural language commands by generalizing manipulation skills to novel objects using verbs, achieving an average accuracy of 76.69% across 13 object categories and 14 verbs and generating usable trajectories for real robot execution.
It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a given object and that are applicable to many objects. We propose a method for generalizing manipulation skills to novel objects using verbs. Our method learns a probabilistic classifier that determines whether a given object trajectory can be described by a specific verb. We show that this classifier accurately generalizes to novel object categories with an average accuracy of 76.69% across 13 object categories and 14 verbs. We then perform policy search over the object kinematics to find an object trajectory that maximizes classifier prediction for a given verb. Our method allows a robot to generate a trajectory for a novel object based on a verb, which can then be used as input to a motion planner. We show that our model can generate trajectories that are usable for executing five verb commands applied to novel instances of two different object categories on a real robot.