A natural language planner interface for mobile manipulators
A natural language planner interface for mobile manipulators
复制标题
用于移动操纵器的自然语言规划器界面
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
N. Roy
中科院分区：
文献类型：
--
作者：
T. Howard;Stefanie Tellex;N. Roy
机器人控制的自然语言接口渴望找到反映指令预期行为的最佳动作序列。这是困难的，因为语言的多样性，环境的多样性和任务的异质性。以前的工作已经表明，从自然语言的解析结构构建的概率图形模型可以用来识别最接近动词短语的运动。然而，这种方法很快就屈服于建筑和搜索可能的行动空间所施加的计算瓶颈。规划约束定义了目标区域，并将环境模型中的允许状态和不允许状态分开，为表示动词短语的含义提供了一种有趣的替代方案。在本文中，我们提出了一个新的模型，称为分布式对应图（DCG）推断最有可能的规划约束的自然语言指令集。然后，轨迹规划器使用这些规划约束来找到类似于指令的动作序列。将识别语言编码的动作的问题分离成规划约束推理和运动规划的各个步骤，使我们能够避免与许多轨迹的生成和评估相关的计算成本。我们目前的实验结果表明，在自然语言理解的效率提高，而不损失的准确性比较实验。
Natural language interfaces for robot control aspire to find the best sequence of actions that reflect the behavior intended by the instruction. This is difficult because of the diversity of language, variety of environments, and heterogeneity of tasks. Previous work has demonstrated that probabilistic graphical models constructed from the parse structure of natural language can be used to identify motions that most closely resemble verb phrases. Such approaches however quickly succumb to computational bottlenecks imposed by construction and search the space of possible actions. Planning constraints, which define goal regions and separate the admissible and inadmissible states in an environment model, provide an interesting alternative to represent the meaning of verb phrases. In this paper we present a new model called the Distributed Correspondence Graph (DCG) to infer the most likely set of planning constraints from natural language instructions. A trajectory planner then uses these planning constraints to find a sequence of actions that resemble the instruction. Separating the problem of identifying the action encoded by the language into individual steps of planning constraint inference and motion planning enables us to avoid computational costs associated with generation and evaluation of many trajectories. We present experimental results from comparative experiments that demonstrate improvements in efficiency in natural language understanding without loss of accuracy.