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Alessandro Devo

3 accepted papers

2022

Autonomous Single-Image Drone Exploration With Deep Reinforcement Learning and Mixed Reality

RA-L 2022

Autonomous exploration is a longstanding goal of the robotics community. Aerial drone navigation has proven to be especially challenging. The stringent requirements on cost, weight, maneuverability, and power consumption do not allow exploration approaches to easily be employed or adapted to differe

Cited by 28SourceScholar
2022

E-VAT: An Asymmetric End-to-End Approach to Visual Active Exploration and Tracking

RA-L 2022

The development of visual tracking systems is becoming a major goal for the Robotics community. Most of the works dealing with this topic focus exclusively on passive tracking, where the target is confined within the camera’s field of view. Only a minority propose active approaches, capable not only

Cited by 24SourceScholar
2020

Deep Reinforcement Learning for Instruction Following Visual Navigation in 3D Maze-Like Environments

RA-L 2020

In this work, we address the problem of visual navigation by following instructions. In this task, the robot must interpret a natural language instruction in order to follow a predefined path in a possibly unknown environment. Despite different approaches have been proposed in the last years, they a

Cited by 25SourceScholar