When I began to study Reinforcement Learning ... The diagram below shows the cycle of the MDP for robotic grasping. The process of exploring the environment, creating a value function representation, ...

Reinforcement learning ... help robots adapt to complex and dynamic environments, such as navigation, manipulation, or coordination. However, RL also poses many challenges for robot control ...

This has prompted exploration of alternative approaches. This project addresses wheeled mobile robot position control using Reinforcement Learning (RL). Conducting the learning process in simulation ...

In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, ...

Continuous reinforcement learning deals with environments where actions are continuous, such as the precise control of robotic arm joints or ... feedback (agent always gets a zero reward unless the ...

In order to achieve high control accuracy and flexibility on a relatively inexpensive robot arm. This paper proposes an improved DDPG (Deep Deterministic Policy Gradient) reinforcement learning ...

Industrial robots deployed ... yet robust, control algorithms in the face of inherent difficulties in modeling all possible system behaviors and the necessity of behavior generalization. Reinforcement ...

This Research Topic seeks to provide a comprehensive overview of the current state-of-the-art in Reinforcement Learning and Foundation Models for robot navigation and control, highlighting the latest ...

A major challenge in modern robotics is to liberate robots from controlled industrial settings ... However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive ...