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.
Given a natural language utterance, grounding methods @cite_5 @cite_32 @cite_27 attempt to associate each word in the utterance with its corresponding referent in this environment model and the robot's symbolic action space. Semantic parsing-based methods @cite_47 @cite_17 @cite_45 similarly map natural language to meaning representations, typically in the form of a lambda calculus. Early work in grounding @cite_33 @cite_11 employs manually engineered correspondences and features between words in a flat representation of the environment. Modern day methods @cite_37 @cite_29 @cite_32 @cite_27 @cite_20 take a statistical approach to language grounding (and similarly for inverse grounding @cite_46 @cite_34 @cite_44 ) that employs probabilistic models that relate words to their corresponding referents according to the hierarchical structure of language, enabling the resolution of complex free-form language. These models are typically learned from annotated natural language corpora as well as through interaction with humans @cite_45 @cite_42 @cite_40 . Probabilistic grounding models have been shown to be effective at interpreting cooking instructions @cite_35 , learning spatial relations in semantic maps @cite_28 @cite_31 , and directing mobile manipulators @cite_4 , among others.
@cite_35 @cite_47 @cite_29 @cite_42 @cite_44 @cite_5 @cite_20 @cite_4 @cite_46 @cite_17 @cite_37 @cite_28 @cite_32 @cite_27 @cite_40 @cite_34 @cite_33 @cite_45 @cite_31 @cite_11
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n order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our speciﬁc approach is to develop models that factor according to the linguistic structure of a command. We ﬁrst describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation. This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environments. Previous approaches have used models with fixed structure to infer the likelihood of a sequence of actions given the environment and the command. In contrast, our framework, called Generalized Grounding Graphs (G3), dynamically instantiates a probabilistic graphical model for a particular natural language command according to the command's hierarchical and compositional semantic structure. Our system performs inference in the model to successfully find and execute plans corresponding to natural language commands such as "Put the tire pallet on the truck." The model is trained using a corpus of commands collected using crowdsourcing. We pair each command with robot actions and use the corpus to learn the parameters of the model. We evaluate the robot's performance by inferring plans from natural language commands, executing each plan in a realistic robot simulator, and asking users to evaluate the system's performance. We demonstrate that our system can successfully follow many natural language commands from the corpus. Speaking using unconstrained natural language is an intuitive and flexible way for humans to interact with robots. Understanding this kind of linguistic input is challenging because diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. We use a probabilistic graphical model that factors into three key components. The first component grounds landmark phrases such as "the computers" in the perceptual frame of the robot by exploiting co-occurrence statistics from a database of tagged images such as Flickr. Second, a spatial reasoning component judges how well spatial relations such as "past the computers" describe a path. Finally, verb phrases such as "turn right" are modeled according to the amount of change in orientation in the path. Our system follows 60 of the directions in our corpus to within 15 meters of the true destination, significantly outperforming other approaches. It is natural for humans to work with abstract plans which are often an intuitive and concise way to represent a task. However, high level task descriptions contain symbols and concepts which need to be grounded within the environment if the plan is to be executed by an autonomous robot. The problem of learning the mapping between abstract plan symbols and their physical instances in the environment is known as the problem of physical symbol grounding. In this paper, we propose a framework for Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE). We associate traces of task demonstration to a sequence of fixations which we call fixation programs and exploit their properties in order to perform physical symbol grounding. We formulate the problem as a probabilistic generative model and present an algorithm for computationally feasible inference over the proposed model. A key aspect of our work is that we estimate fixation locations within the environment which enables the appearance of symbol instances to be learnt. Instance learning is a crucial ability when the robot does not have any knowledge about the model or the appearance of the symbols referred to in the plan instructions. We have conducted human experiments and demonstrate that GLIDE successfully grounds plan symbols and learns the appearance of their instances, thus enabling robots to autonomously execute tasks in initially unknown environments.