We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

The state-of-the-art reinforcement learning (RL) techniques have made innumerable
advancements in robot control, especially in combination with deep neural networks …

Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …

The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a
notoriously difficult problem domain for the application of neural networks. We identify a …

Abstract RoboCup 3D Soccer Simulation is a robot soccer competition based on a high-
fidelity simulator with autonomous humanoid agents, making it an interesting testbed for …

This paper proposes a modular framework to generate robust biped locomotion using a tight
coupling between an analytical walking approach and deep reinforcement learning. This …

This article contributes with a methodology based on deep reinforcement learning to
develop running skills in a humanoid robot with no prior knowledge. Specifically, the …

Since its inception in 1997, RoboCup has developed into a truly unique and long-standing
research community advancing robotics and artificial intelligence through various …

This work seeks to design and implement a humanoid robotic kick for situations where the
robot is moving for the RoboCup simulation 3D robotic soccer league. It employs …

In the current level of evolution of Soccer 3D, motion control is a key factor in team's
performance. Recent works takes advantages of model-free approaches based on Machine …