Motion control is crucial for multilegged robot locomotion and task completion. This study
aims to address the fundamental challenges of inadequate foot tracking and weak leg …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

In this letter, we present a method for integrating central pattern generators (CPGs), ie
systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to …

Reinforcement learning (RL) has become a promising approach to developing controllers
for quadrupedal robots. Conventionally, an RL design for locomotion follows a position …

Learning multiple gaits is non-trivial for legged robots, especially when encountering
different terrains and velocity commands. In this work, we present an end-to-end training …

Deep reinforcement learning has recently emerged as an appealing alternative for legged
locomotion over multiple terrains by training a policy in physical simulation and then …

Robots are traditionally bounded by a fixed embodiment during their operational lifetime,
which limits their ability to adapt to their surroundings. Co-optimizing control and …

Controlled execution of dynamic motions in quadrupedal robots, especially those with
articulated soft bodies, presents a unique set of challenges that traditional methods struggle …

We propose a simple imitation learning procedure for learning locomotion controllers that
can walk over very challenging terrains. We use trajectory optimization (TO) to produce a …

The dynamics of hydraulic robots are complicated due to the closed-chain joints formed by
cylinder articulation. This article is focused on presenting a model-based control framework …