An Implementation of A Reinforcement Learning-Assisted Robot Task Motion Generation Algorithm

Key Points

• The aim is to implement a method for generating walking policies in humanoid robots using reinforcement learning.
• Developed a reinforcement learning-assisted algorithm for walking tasks.
• Integrated the walking policy into the HRP-2 robot's control system.
• Conducted walking simulations and experiments to test the implementation.
• The robot successfully performed mobility tasks during simulations.
• Demonstrated improved task performance compared to previous methods.