Active simultaneous localization and mapping (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Simultaneous localization and mapping responds to the problem of building a map of the
environment without any prior information and based on the data obtained from one or more …

This work presents a modular and hierarchical approach to learn policies for exploring 3D
environments, calledActive Neural SLAM'. Our approach leverages the strengths of both …

We propose a dual-resolution scheme to achieve time-efficient autonomous exploration with
one or many robots. The scheme maintains a high-resolution local map of the robot's …

Exploring and mapping previously unknown environments while avoiding collisions with
obstacles is a fundamental task for autonomous robots. In scenarios where this needs to be …

What goals should a multi-goal reinforcement learning agent pursue during training in long-
horizon tasks? When the desired (test time) goal distribution is too distant to offer a useful …

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot
applications. One of the key challenges is how to explore environments efficiently with …

This paper introduces recently developed Aquila Optimization Algorithm specifically
configured for Multi-Robot space exploration. The proposed hybrid framework “Coordinated …

This paper surveys the supportive technologies currently available for ground mobile robots
used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of …

We address the problem of active mapping with a continually-learned neural scene
representation, namely Active Neural Mapping. The key lies in actively finding the target …