Trajectory Planning for a Robotic Arm Using Reinforcement Learning

Abstract: Reinforcement Learning has brought about a transformation in robotics, thanks to its ability to develop efficient control techniques through autonomous learning. In particular, Reinforcement Learning has proven to be successful in tasks such as reaching objects with robotic arms. In this work, a solution is developed for training this task in simulated environments, and an experimental setup is established to compare the performance of various model-free algorithms.

It is demonstrated that PPO achieves the best results, while SAC exhibits instability in environments with Dense rewards. Furthermore, it is concluded that a Sparse reward is sufficient to solve the task in environments with a precision of 5 cm.