论文标题
通过递归信念更新接地机器人室内导航的隐性目标描述
Grounding Implicit Goal Description for Robot Indoor Navigation Via Recursive Belief Update
论文作者
论文摘要
由于人类对导航约束的了解，基于自然语言的机器人导航仍然是一个具有挑战性的问题，目的地与机器人知识库不直接兼容。在本文中，我们旨在将自然目的地命令转换为感兴趣地图的高级机器人导航计划。我们确定了目的地描述的语法关联段，并递归地应用了它们，以更新给定映射上的区域的信念分布。我们使用单步信念更新数据集训练目标接地模型，以确切，接近和定向修饰符类型。我们在由80个领域组成的办公室中演示了有关现实世界导航任务的方法。离线实验结果表明，我们的方法可以直接从人类询问的闻所未闻，长和复合文本命令中提取目标目的地。这使用户能够以一般和自然形式为机器人指定其目标目标。硬件实验结果还表明，设计模型为将导航目标指定到服务机器人带来了很多便利。
Natural language-based robotic navigation remains a challenging problem due to the human knowledge of navigation constraints, and destination is not directly compatible with the robot knowledge base. In this paper, we aim to translate natural destination commands into high-level robot navigation plans given a map of interest. We identify grammatically associated segments of destination description and recursively apply each of them to update a belief distribution of an area over the given map.We train a destination grounding model using a dataset of single-step belief update for precise, proximity, and directional modifier types. We demonstrate our method on real-world navigation task in an office consisting of 80 areas. Offline experimental results show that our method can directly extract goal destination from unheard, long, and composite text commands asked by humans. This enables users to specify their destination goals for the robot in general and natural form. Hardware experiment results also show that the designed model brings much convenience for specifying a navigation goal to a service robot.
