Optimizing the Trajectory of Robotic Manipulator: Reinforcement Learning for the Generation of Initial Guess

Abstract

This paper describes the challenge of trajectory planning for robotic manipulators operating in obstacle-rich environments. The goal is to propose a novel approach that integrates the TrajOpt algorithm with reinforcement learning (RL) to improve the efficiency and accuracy of motion planning. TrajOpt, a numerical optimization-based method, generates successful trajectories, while RL produces high-quality initial trajectory estimates, serving as strong starting points for optimization. This integration enables TrajOpt to find feasible paths more efficiently and reduces collision risks. RL further refines initial trajectories, enhancing navigation in complex environments. Experimental results highlight the benefits of merging machine learning with traditional optimization methods. The RL-based approach achieved an 82% success rate in navigating obstacle-dense environments, outperforming the classical RRTConnect method, which achieved 68%. The proposed system improves planning efficiency and reliability in complex scenarios with obstacles by using reinforcement learning (RL) alongside TrajOpt. This research offers valuable insights into improving robotic manipulator performance and lays a foundation for future advancements in motion planning. It demonstrates that integrating reinforcement learning with existing algorithms can drive significant progress in addressing the complexities of navigating obstacle-laden environments, opening new directions for research and development.