Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning

TL;DR

This paper presents LMCR, a method that enables robots to interpret incomplete natural language instructions by using environmental context and commonsense reasoning learned from large textual corpora, improving robot-human interaction.

Contribution

The paper introduces a novel approach combining language parsing and commonsense reasoning to fill in missing instruction details using environmental cues and large-scale language models.

Findings

Robots can automatically infer missing instruction details from context.

Commonsense reasoning models improve task execution accuracy.

Feasibility demonstrated through experiments with web-trained language models.

Abstract

Enabling robots to understand instructions provided via spoken natural language would facilitate interaction between robots and people in a variety of settings in homes and workplaces. However, natural language instructions are often missing information that would be obvious to a human based on environmental context and common sense, and hence does not need to be explicitly stated. In this paper, we introduce Language-Model-based Commonsense Reasoning (LMCR), a new method which enables a robot to listen to a natural language instruction from a human, observe the environment around it, and automatically fill in information missing from the instruction using environmental context and a new commonsense reasoning approach. Our approach first converts an instruction provided as unconstrained natural language into a form that a robot can understand by parsing it into verb frames. Our approach then fills in missing information in the instruction by observing objects in its vicinity and leveraging commonsense reasoning. To learn commonsense reasoning automatically, our approach distills knowledge from large unstructured textual corpora by training a language model. Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.