Attention-Based Reinforcement Learning with Center Reward Classification for Redundant Manipulators Motion Optimization

Reliable and efficient obstacle-avoidance motion planning for redundant manipulators remains challenging, especially in environments with irregular obstacles and high-dimensional constraints. Although deep reinforcement learning (DRL) offers promising solutions, existing methods still suffer from slow convergence and suboptimal trajectory quality. This paper accounts for practical path-length and time constraints and proposes an improved DRL-based approach that exhibits significantly faster conv