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 …

Recent work in visual representation learning for robotics demonstrates the viability of
learning from large video datasets of humans performing everyday tasks. Leveraging …

We study the problem of learning a range of vision-based manipulation tasks from a large
offline dataset of robot interaction. In order to accomplish this, humans need easy and …

Systems for language-guided human-robot interaction must satisfy two key desiderata for
broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction …

When humans design cost or goal specifications for robots, they often produce specifications
that are ambiguous, underspecified, or beyond planners' ability to solve. In these cases …

We are increasingly surrounded by artificially intelligent technology that takes decisions and
executes actions on our behalf. This creates a pressing need for general means to …

In classic instruction following, language like" I'd like the JetBlue flight" maps to actions (eg,
selecting that flight). However, language also conveys information about a user's underlying …

This paper investigates how to efficiently transition and update policies, trained initially with
demonstrations, using off-policy actor-critic reinforcement learning. It is well-known that …

Building agents capable of understanding language instructions is critical to effective and
robust human-AI collaboration. Recent work focuses on training these agents via …

Abstract Human-Agent Interaction is at the forefront of rapid development, with integrating
deep learning techniques into natural language processing representing significant …