Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks

Title: Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks Summary: In this research, I aim to explore how robots can learn to perform complex tasks more effectively by combining reinforcement learning, behavior trees, and genetic programming. The idea is to help robots simultaneously figure out not just what actions to take, but also the best sequence of those actions to complete tasks like grasping or assembling objects. By using reinforcement learning, the robot can learn from trial and error, improving its decision-making over time. Behavior trees offer a structured way to define and adapt complex behaviors, making the robot's actions more flexible. Meanwhile, genetic programming will be used to evolve and optimize these behaviors, helping the robot find the most efficient strategies even in unpredictable environments. Ultimately, this research aims to create robots that are not only more capable but also more adaptable to the challenges they encounter in the real world.