用动词让机器人学会对新物体执行动作
Skill Generalization with Verbs
- 通过概率分类器判断物体轨迹是否符合特定动词
- 在13类新物体上平均准确率达76.69%
- 实机验证可生成五种动词指令的可行轨迹
机器人理解人类自然语言命令至关重要。这些命令通常包含表示对某物体执行动作的动词，且适用于多种物体。本文提出一种基于动词的操纵技能泛化方法：学习一个概率分类器，判断给定物体运动轨迹能否由特定动词描述。该分类器在13类新物体和14个动词上平均准确率达76.69%。随后，在物体运动学空间中进行策略搜索，找到使分类器预测值最大的轨迹。该方法使机器人能基于动词生成新物体的运动轨迹，作为运动规划输入。实验表明，模型可在真实机器人上生成可用于执行五种动词命令的轨迹，适用对象为两类不同物体的新实例。
原文摘要 · Abstract (English)
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.
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