论文标题
自然语言机器人指令以下几个射击对象接地和映射以下
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction Following
论文作者
论文摘要
我们研究了学习机器人政策的问题，以遵循自然语言指示，可以轻松扩展以推理新对象。我们介绍了一些从增强现实数据训练的语言条件的对象接地方法，该方法使用示例来识别对象并将其与他们的说明中的提及保持一致。我们提出了一个博学的地图表示形式，该表示对象位置及其指示使用，并从我们的几片接地输出中构造它。我们将这种映射方法集成到遵循指令策略中，从而使其可以通过简单地添加示例来推理在测试时间之前看不见的对象。我们评估学习映射原始观察和指示以连续控制物理四轮驱动器的任务。即使在训练期间，我们的方法在存在新物体的情况下，我们的方法在存在新物体的情况下大大优于先前的艺术状态。
We study the problem of learning a robot policy to follow natural language instructions that can be easily extended to reason about new objects. We introduce a few-shot language-conditioned object grounding method trained from augmented reality data that uses exemplars to identify objects and align them to their mentions in instructions. We present a learned map representation that encodes object locations and their instructed use, and construct it from our few-shot grounding output. We integrate this mapping approach into an instruction-following policy, thereby allowing it to reason about previously unseen objects at test-time by simply adding exemplars. We evaluate on the task of learning to map raw observations and instructions to continuous control of a physical quadcopter. Our approach significantly outperforms the prior state of the art in the presence of new objects, even when the prior approach observes all objects during training.
