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 …

Performing agile navigation with four-legged robots is a challenging task because of the
highly dynamic motions, contacts with various parts of the robot, and the limited field of view …

The interest in exploring planetary bodies for scientific investigation and in situ resource
utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art …

With the rapid development of space crawling robotics technology, tactile perception, a
significant source for the robot to sense the external environment, has become the preferred …

Legged robots have enormous potential in their range of capabilities, from navigating
unstructured terrains to high-speed running. However, designing robust controllers for highly …

The common approach for local navigation on challenging environments with legged robots
requires path planning, path following and locomotion, which usually requires a locomotion …

So-called robot–object Lagrangian systems consist of a class of nonsmooth underactuated
complementarity Lagrangian systems, with a specific structure: an “object” and a “robot” …

Deep reinforcement learning can enable robots to autonomously acquire complex behaviors
such as legged locomotion. However, RL in the real world is complicated by constraints on …

Unlike the spherical gravitational field of planets and other large solar system bodies, the
gravitational field of asteroids is irregular and weak. It is challenging for a planetary rover to …

Quadrupedal landing is a complex process involving large impacts, elaborate contact
transitions, and is a crucial recovery behavior observed in many biological animals. This …