This article surveys the use of natural language in robotics from a robotics point of view. To
use human language, robots must map words to aspects of the physical world, mediated by …

Learning from Demonstration (LfD) explores techniques for learning a task policy from
examples provided by a human teacher. The field of LfD has grown into an extensive body …

It is important for a robot to be able to interpret natural language commands given by a
human. In this paper, we consider performing a sequence of mobile manipulation tasks with …

To perform tasks specified by natural language instructions, autonomous agents need to
extract semantically meaningful representations of language and map it to visual elements …

This article presents a new research area called interactive task learning (ITL), in which an
agent actively tries to learn not just how to perform a task better but the actual definition of a …

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that
are capable of solving multiple tasks and reach a diverse set of objectives. How to\textit …

In this paper, a robot learning approach is proposed which integrates Visuospatial Skill
Learning, Imitation Learning, and conventional planning methods. In our approach, the …

As robots become more affordable and more common in everyday life, particularly in
assistive contexts, there will be an ever-increasing demand for adaptive behavior that is …

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that
are capable of solving multiple tasks and reach a diverse set of objectives. How to\textit …

This paper describes a syntactic approach to imitation learning that captures important task
structures in the form of probabilistic activity grammars from a reasonably small number of …