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.

翻译：机器人必须能够理解人类发出的自然语言指令。此类指令通常包含动词，这些动词表示应对给定对象执行何种动作，并且适用于多种对象。我们提出了一种利用动词将操作技能泛化至新对象的方法。该方法学习一个概率分类器，用于判断给定物体轨迹是否可由特定动词描述。实验表明，该分类器能准确泛化至新物体类别，在13个物体类别和14个动词上的平均准确率达到76.69%。随后，我们在物体运动学空间进行策略搜索，以找到使特定动词的分类器预测概率最大化的物体轨迹。该方法使机器人能够基于动词为新物体生成轨迹，该轨迹可作为运动规划器的输入。我们通过真实机器人实验证明，该模型生成的轨迹能够成功执行应用于两个不同物体类别新实例的五种动词指令。