In this paper, a review on the three most important communication techniques (ground,
aerial, and underwater vehicles) has been presented that throws light on trajectory planning …

Autonomous driving is of great interest to industry and academia alike. The use of machine
learning approaches for autonomous driving has long been studied, but mostly in the …

Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

Although cameras are ubiquitous, robotic platforms typically rely on active sensors like
LiDAR for direct 3D perception. In this work, we propose a novel self-supervised monocular …

Two less addressed issues of deep reinforcement learning are (1) lack of generalization
capability to new goals, and (2) data inefficiency, ie, the model requires several (and often …

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's
objective is to perceive the geometry of the environment in order to plan collision-free paths …

Deep reinforcement learning has emerged as a promising and powerful technique for
automatically acquiring control policies that can process raw sensory inputs, such as …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

Abstract We introduce Interactive Question Answering (IQA), the task of answering questions
that require an autonomous agent to interact with a dynamic visual environment. IQA …

Self-supervised learning is showing great promise for monocular depth estimation, using
geometry as the only source of supervision. Depth networks are indeed capable of learning …