gComm is a step towards developing a robust platform to foster research in
grounded language acquisition in a more challenging and realistic setting. It
comprises a 2-d grid environment with a set of agents (a stationary speaker and
a mobile listener connected via a communication channel) exposed to a
continuous array of tasks in a partially observable setting. The key to solving
these tasks lies in agents developing linguistic abilities and utilizing them
for efficiently exploring the environment. The speaker and listener have access
to information provided in different modalities, i.e. the speaker's input is a
natural language instruction that contains the target and task specifications
and the listener's input is its grid-view. Each must rely on the other to
complete the assigned task, however, the only way they can achieve the same, is
to develop and use some form of communication. gComm provides several tools for
studying different forms of communication and assessing their generalization.

gComm 是一种平台，通过基于代理的语言获取来模拟更具挑战性和现实的环境，以解决一系列任务，掌握语言技能进行有效环境探索，并提供了许多工具用于研究不同形式的通信和评估其泛化能力。

gComm：探究基于场景的语言习得中泛化的环境

gComm: An environment for investigating generalization in Grounded  Language Acquisition

Many task domains require robots to interpret and act upon natural language
commands which are given by people and which refer to the robot's physical
surroundings. Such interpretation is known variously as the symbol grounding
problem, grounded semantics and grounded language acquisition. This problem is
challenging because people employ diverse vocabulary and grammar, and because
robots have substantial uncertainty about the nature and contents of their
surroundings, making it difficult to associate the constitutive language
elements (principally noun phrases and spatial relations) of the command text
to elements of those surroundings. Symbolic models capture linguistic structure
but have not scaled successfully to handle the diverse language produced by
untrained users. Existing statistical approaches can better handle diversity,
but have not to date modeled complex linguistic structure, limiting achievable
accuracy. Recent hybrid approaches have addressed limitations in scaling and
complexity, but have not effectively associated linguistic and perceptual
features. Our framework, called Generalized Grounding Graphs (G^3), addresses
these issues by defining a probabilistic graphical model dynamically according
to the linguistic parse structure of a natural language command. This approach
scales effectively, handles linguistic diversity, and enables the system to
associate parts of a command with the specific objects, places, and events in
the external world to which they refer. We show that robots can learn word
meanings and use those learned meanings to robustly follow natural language
commands produced by untrained users. We demonstrate our approach for both
mobility commands and mobile manipulation commands involving a variety of
semi-autonomous robotic platforms, including a wheelchair, a micro-air vehicle,
a forklift, and the Willow Garage PR2.

该研究提出了一种名为广义接地图 (G^3) 的框架，它通过将自然语言命令的语言分析结构动态地映射到概率图模型中，实现了语言与感知特征的有效关联。该方法能够处理语言多样性，提高了可扩展性和准确性，使机器人能够学习词汇，并根据学习到的词汇跟随未经训练的用户所发出的自然语言命令。