Imitation learning for natural language direction following through unknown environments
Imitation learning for natural language direction following through unknown environments
复制标题
通过未知环境进行自然语言指导的模仿学习
DOI：
10.1109/icra.2013.6630702
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Stentz
中科院分区：
文献类型：
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作者：
Felix Duvallet;T. Kollar;A. Stentz
在人类机器人团队中使用口头指令有望使未经训练的用户能够以自然和直观的方式有效地控制复杂的机器人系统。为机器人提供理解自然语言方向的能力将使在非专业未知环境中工作的人类机器人团队能够毫不费力地协调。然而，通过未知环境遵循自然语言方向需要理解语言的含义，使用部分语义世界模型来生成世界中的动作，并对尚未检测到的环境和地标进行推理。我们解决的问题，通过复杂的未知环境中的机器人遵循自然语言的方向。利用空间语言的结构，我们可以框架方向跟踪作为一个不确定性下的顺序决策问题。我们学习一个策略，它预测一系列的行动，通过探索环境和发现地标，必要时回溯，并明确声明何时到达目的地。我们使用模仿学习来训练策略，使用人们遵循方向的示范。通过在未知的环境中进行显式训练，我们可以推广到以前没有遇到过的情况。
The use of spoken instructions in human-robot teams holds the promise of enabling untrained users to effectively control complex robotic systems in a natural and intuitive way. Providing robots with the capability to understand natural language directions would enable effortless coordination in human robot teams that operate in non-specialized unknown environments. However, natural language direction following through unknown environments requires understanding the meaning of language, using a partial semantic world model to generate actions in the world, and reasoning about the environment and landmarks that have not yet been detected. We address the problem of robots following natural language directions through complex unknown environments. By exploiting the structure of spatial language, we can frame direction following as a problem of sequential decision making under uncertainty. We learn a policy which predicts a sequence of actions that follow the directions by exploring the environment and discovering landmarks, backtracking when necessary, and explicitly declaring when it has reached the destination. We use imitation learning to train the policy, using demonstrations of people following directions. By training explicitly in unknown environments, we can generalize to situations that have not been encountered previously.