In this contribution, we present our research line on Deep Reinforcement Learning approaches for robot navigation, in particular: Target-Driven Visual Navigation and Visual Active Tracking. We assess our methods capabilities in several challenging scenarios and in a number of environments previously unseen during training. Finally, we also prove that they can be effectively deployed in real-world settings on real platforms.https://youtu.be/yuFUo1gYGs
In this paper, we propose a two-stage learning framework for visual navigation in which the experien...
In this paper, we propose a two-stage learning framework for visual navigation in which the experien...
Navigation is the fundamental capability of mobile robots which allows them to move fromone point to...
Visual tracking approaches have recently gained significant attention from the research community. T...
Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environment...
This paper aims at highlighting cutting-edge research results in the field of visual tracking by dee...
Visual navigation is essential for many applications in robotics, from manipulation, through mobile ...
This paper presents a spatial navigation task on a mobile robot by employing a deep reinforcement le...
© 2019 IEEE. The paper is concerned with the autonomous navigation of mobile robot from the current ...
A study is presented on visual navigation of wheeled mobile robots (WMR) using deep reinforcement le...
In this work, we address generalization in targetdriven visual navigation by proposing a novel archi...
Abstract Deep reinforcement learning‐based methods employ an ample amount of computational power tha...
Reliable indoor navigation in the presence of dynamic obstacles is an essential capability for mobil...
Artificially intelligent agents with some degree of autonomy in the real world must learn to complet...
Artificially intelligent agents with some degree of autonomy in the real world must learn to complet...
In this paper, we propose a two-stage learning framework for visual navigation in which the experien...
In this paper, we propose a two-stage learning framework for visual navigation in which the experien...
Navigation is the fundamental capability of mobile robots which allows them to move fromone point to...
Visual tracking approaches have recently gained significant attention from the research community. T...
Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environment...
This paper aims at highlighting cutting-edge research results in the field of visual tracking by dee...
Visual navigation is essential for many applications in robotics, from manipulation, through mobile ...
This paper presents a spatial navigation task on a mobile robot by employing a deep reinforcement le...
© 2019 IEEE. The paper is concerned with the autonomous navigation of mobile robot from the current ...
A study is presented on visual navigation of wheeled mobile robots (WMR) using deep reinforcement le...
In this work, we address generalization in targetdriven visual navigation by proposing a novel archi...
Abstract Deep reinforcement learning‐based methods employ an ample amount of computational power tha...
Reliable indoor navigation in the presence of dynamic obstacles is an essential capability for mobil...
Artificially intelligent agents with some degree of autonomy in the real world must learn to complet...
Artificially intelligent agents with some degree of autonomy in the real world must learn to complet...
In this paper, we propose a two-stage learning framework for visual navigation in which the experien...
In this paper, we propose a two-stage learning framework for visual navigation in which the experien...
Navigation is the fundamental capability of mobile robots which allows them to move fromone point to...