The evolving field of human-robot interaction (HRI) necessitates that we better understand
how social robots operate and interact with humans. This sco** review provides an …

We present a holistic data-driven approach to image description generation, exploiting the
vast amount of (noisy) parallel image data and associated natural language descriptions …

It is natural and efficient to use human natural language (NL) directly to instruct robot task
executions without prior user knowledge of instruction patterns. Currently, NL-instructed …

Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks.
Although meta-learning is a method to endow neural networks with useful inductive biases …

Grounded language learning bridges words like 'red'and 'square'with robot perception. The
vast majority of existing work in this space limits robot perception to vision. In this paper, we …

Natural-language-facilitated human–robot cooperation refers to using natural language to
facilitate interactive information sharing and task executions with a common goal constraint …

Abstract Interactive Task Learning (ITL) focuses on learning the definition of tasks through
online natural language instruction in real time. Learning the correct grounded meaning of …

In this work, we present methods for using human-robot dialog to improve language
understanding for a mobile robot agent. The agent parses natural language to underlying …

Active learning identifies data points from a pool of unlabeled examples whose labels, if
made available, are most likely to improve the predictions of a supervised model. Most …

There has been substantial work in recent years on grounded language acquisition, in
which language and sensor data are used to create a model relating linguistic constructs to …