Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning

Online trajectory planning in unstructured environments poses significant challenges for mobile robots, particularly when navigating complex obstacles. Traditional learning-from-demonstration (LfD) methods depend on offline datasets, limiting their ability to adapt to varying obstacle shapes and dynamic conditions. To address these limitations, we propose a novel motion planning framework that combines global trajectory generation with local adaptability. Dynamic Movement Primitives (DMPs) are employed to generate global trajectories based on demonstrations, while Task-Parameterized Potential Fields (TPPFs) enhance local adaptability. The Policy Improvement through Path Integrals (PI$^{2}$) algorithm is utilized to optimize model parameters. The TPPF framework consists of two key components: (a) an obstacle avoidance field, which accounts for the robot's size, obstacle dimensions, and relative distances, allowing effective volumetric avoidance without extensive modeling; and (b) an attractive field, which directs the robot toward task-specific goals while steering it away from undesirable paths. By leveraging the PI$^{2}$ algorithm, model parameters are optimized to produce trajectories that preserve the characteristics of demonstrated motions, while improving obstacle avoidance and task-oriented navigation. Experiments conducted in both simulations and dynamic real-world scenarios validate the proposed framework's effectiveness, demonstrating smoother trajectories and enhanced obstacle avoidance compared to baseline approaches.