This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Active learning is a decision-making process. In both abstract and physical settings, active
learning demands both analysis and action. This is a review of active learning in robotics …

For robots to follow instructions from people, they must be able to connect the rich semantic
information in human vocabulary, eg" can you get me the pink stuffed whale?" to their …

Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …

Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

State-of-the-art navigation methods leverage a spatial memory to generalize to new
environments, but their occupancy maps are limited to capturing the geometric structures …

Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end
but struggle to perform well in freely traversable environments. Inspired by the robotics …

Simultaneous localization and mapping (SLAM) remains challenging for a number of
downstream applications, such as visual robot navigation, because of rapid turns …

Abstract 3D scene graphs (3DSGs) are an emerging description; unifying symbolic,
topological, and metric scene representations. However, typical 3DSGs contain hundreds of …

This paper presents INVIGORATE, a robot system that interacts with human through natural
language and grasps a specified object in clutter. The objects may occlude, obstruct, or even …