Robot arm control is a technological challenge where an algorithm needs to learn a deep understanding of spatial navigation. In particular, spatial navigation requires learning the relationship between the motor joint angular positions and the Cartesian coordinates of the robot. Trajectory planning is an even more complex challenge, where the algorithm must create a trajectory between two coordinates that does not cause a collision. State-of-the-art algorithms capable of solving trajectory planning are based on deep Reinforcement Learning (RL). These algorithms achieve high accuracy but suffer from high computational complexity. This paper proposes to use a genetic RL algorithm, the Tangled Program Graphs (TPGs), to solve trajectory planning. Using a genetic process, the TPGs generate a graph of programs with low inference complexity. On a first trajectory planning problem, the algorithm used achieves performance close to the state-of-the-art, but with a 100 less execution time and a 20× smaller model size. On a second and more difficult problem, the TPGs are not able to learn with efficiency. We propose a hybrid solution that mixes the TPGs and a state-of-the-art deep RL algorithm, the Soft Actor-Critic (SAC). This solution achieves better performance than the state-of-the-art for both problems, with 6 to 20 times faster execution times.

Robot control presents a formidable technological challenge in today's landscape. At its core, this challenge entails the development of algorithms that possess a deep understanding of spatial relationships. This understanding is crucial for orchestrating the coordinated movements of the various motors of the robot. As robots become increasingly ubiquitous across industries, there is a pressing need to optimize the computational costs associated with controlling them. This optimization ensures efficiency without compromising performance, a balancing act essential for widespread adoption and practical implementation.

Some recent techniques, such as Deep Learning, can efficiently control robot models. Deep