Systems of natural-language-facilitated human-robot cooperation: A review Artificial Intelligence

Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.

Learning Grounded Language through Situated Interactive Instruction

AAAI Conferences

We present an approach for learning grounded language from mixed-initiative human-robot interaction. Prior work on learning from human instruction has concentrated on acquisition of task-execution knowledge from domain-specific language. In this work, we demonstrate acquisition of linguistic, semantic, perceptual, and procedural knowledge from mixed-initiative, natural language dialog. Our approach has been instantiated in a cognitive architecture, Soar, and has been deployed on a table-top robotic arm capable of picking up small objects. A preliminary analysis verifies the ability of the robot to acquire diverse knowledge from human-robot interaction.

Communicating with Executable Action Representations

AAAI Conferences

Natural language instructions are often underspecified and imprecise which makes them hard to understand for an artificial agent. In this article we present a system of connected knowledge representations that is used to control a robot through instructions. As actions are a key component of instructions and the robot's behavior the representation of action is central in our approach. First, the system consists of a conceptual schema representation which provides a parameter interface for action. Second, we present an intermediate representation of the temporal structure of action and show how this generic action structure can be mapped to detailed action controllers as well as language.

Combining World and Interaction Models for Human-Robot Collaborations

AAAI Conferences

As robotic technologies mature, we can imagine an increasing number of applications in which robots could soon prove to be useful in unstructured human environments. Many of those applications require a natural interface between the robot and untrained human users or are possible only in a human-robot collaborative scenario. In this paper, we study an example of such scenario in which a visually impaired person and a robotic guide collaborate in an unfamiliar environment. We then analyze how the scenario can be realized through language- and gesture-based human-robot interaction, combined with semantic spatial understanding and reasoning, and propose an integration of semantic world model with language and gesture models for several collaboration modes. We believe that this way practical robotic applications can be achieved in human environments with the use of currently available technology.

Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions Artificial Intelligence

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. However, existing methods still attempt to reason over a representation of the environment that is flat and unnecessarily detailed, which limits scalability. An open problem is then to develop methods capable of producing the most compact environment model sufficient for accurate and efficient natural language understanding. We propose a model that leverages environment-related information encoded within instructions to identify the subset of observations and perceptual classifiers necessary to perceive a succinct, instruction-specific environment representation. The framework uses three probabilistic graphical models trained from a corpus of annotated instructions to infer salient scene semantics, perceptual classifiers, and grounded symbols. Experimental results on two robots operating in different environments demonstrate that by exploiting the content and the structure of the instructions, our method learns compact environment representations that significantly improve the efficiency of natural language symbol grounding.