As robots take on more complex tasks, traditional programming methods become insufficient. Reinforcement learning (RL) is a machine learning technique designed to address this challenge by programming robot behavior. With RL in simulation, robots can train in any virtual environment through trial and error, enhancing their skills in control, path planning, manipulation, and more.
The RL model gets rewarded for desired actions, so it’s constantly adapting and improving. This helps robots develop sophisticated gross and fine motor skills needed for real-world automation tasks such as grasping novel objects, quadrupedal walking, and learning complex manipulation skills.
By continuously refining control policies based on rewards and analyzing their actions, RL can also help robots adjust to new situations and unforeseen challenges, making them more adaptable for real-world tasks.