Navigation is one of the most heavily studied problems in robotics and is conventionally
approached as a geometric mapping and planning problem. However, real-world navigation …

Predicting human trajectories is an important component of autonomous moving platforms,
such as social robots and self-driving cars. Human trajectories are affected by both the …

We present an interpretable framework for path prediction that leverages dependencies
between agents' behaviors and their spatial navigation environment. We exploit two sources …

Traversability illustrates the difficulty of driving through a specific region and encompasses
the suitability of the terrain for traverse based on its physical properties, such as slope and …

We present semi-supervised deep learning approaches for traversability estimation from
fisheye images. Our method, GONet, and the proposed extensions leverage Generative …

Drones with obstacle avoidance capabilities have attracted much attention from researchers
recently. They typically adopt either supervised learning or reinforcement learning (RL) for …

This paper proposes deep learning-based footstep planning using Generative Adversarial
Networks (GANs) for the indoor navigation of humanoid robots. The GAN-based architecture …

This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for
path planning in support of smart mobility applications such as indoor and outdoor …

Graphs in real-world applications are dynamic both in terms of structures and inputs.
Information discovery in such networks, which present dense and deeply connected patterns …

Building on the success of deep learning, two modern approaches to learn a probability
model from the data are Generative Adversarial Networks (GANs) and Variational …