The problem of autonomous navigation of a ground vehicle in unstructured environments is
both challenging and crucial for the deployment of this type of vehicle in real-world …

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 …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

With the rapid development of machine learning, its powerful function in the machine vision
field is increasingly reflected. The combination of machine vision and robotics to achieve the …

Mobile robots operating in unknown urban environments encounter a wide range of
complex terrains to which they must adapt their planned trajectory for safe and efficient …

In this work, we show how to learn a visual walking policy that only uses a monocular RGB
camera and proprioception. Since simulating RGB is hard, we necessarily have to learn …

Navigation in natural outdoor environments requires a robust and reliable traversability
classification method to handle the plethora of situations a robot can encounter. Binary …

This work reports ondeveloping a deep inverse reinforcement learning method for legged
robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive …

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness,
deformability, etc.) of complex outdoor terrains directly from robot-terrain interactions through …

Understanding the terrain in the upcoming path of a ground robot is one of the most
challenging problems in field robotics. Terrain and traversability analysis is a …