Deep reinforcement learning (DRL) offers a promising approach for the synthesis of control
policies for legged robots locomotion. However, it remains challenging to learn policies that …

In nature, legged animals have developed the ability to adapt to challenging terrains through
perception, allowing them to plan safe body and foot trajectories in advance, which leads to …

For legged robots to match the athletic capabilities of humans and animals, they must not
only produce robust periodic walking and running, but also seamlessly switch between …

Legged locomotion is a challenging task for learning algorithms, especially when the task
requires a diverse set of primitive behaviors. To solve these problems, we introduce a …

Legged robots need to be capable of walking on diverse terrain conditions. In this paper, we
present a novel reinforcement learning framework for learning locomotion on non-rigid …

Recent advancements in legged locomotion research have made legged robots a preferred
choice for navigating challenging terrains when compared to their wheeled counterparts …

For a robot, the ability to get from one place to another is one of the most basic skills.
However, locomotion on legged robots is a challenging multidimensional control problem …

Can a quadrupedal robot perform bipedal motions like humans? Although developing
human-like behaviors is more often studied on costly bipedal robot platforms, we present a …

This thesis presents methods for training intelligent robotic systems to navigate challenging,
diverse environments. In particular it focuses on legged locomotion for hexapods rather than …

Legged robotic technologies have moved out of the lab to operate in real environments,
characterized by a wide variety of unpredictable irregularities and disturbances, all this in …