Providing mobile robots with autonomous capabilities is advantageous. It allows one to
dispense with the intervention of human operators, which may prove beneficial in economic …

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement
Learning (DRL) has received significant attention because of its strong representation and …

Goal-conditioned policies for robotic navigation can be trained on large, unannotated
datasets, providing for good generalization to real-world settings. However, particularly in …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

Mobile robots, including ground robots, underwater robots, and unmanned aerial vehicles,
play an increasingly important role in people's work and lives. Path planning and obstacle …

Moving in complex environments is an essential capability of intelligent mobile robots.
Decades of research and engineering have been dedicated to developing sophisticated …

General-purpose pre-trained models (" foundation models") have enabled practitioners to
produce generalizable solutions for individual machine learning problems with datasets that …

Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

The history of learning for control has been an exciting back and forth between two broad
classes of algorithms: planning and reinforcement learning. Planning algorithms effectively …

In the first part, this article presents an overview of Guidance, Navigation and Control (GNC)
methodologies developed for space manipulators to perform in-orbit robotic missions …