As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Method (iCEM) have recently demonstrated promising potential for low-level real-time planning by leveraging efficient knowledge reuse strategies to improve performance. Although effective in many control tasks, iCEM's performance can be constrained in more complex scenarios, particularly those requiring stacking, sliding, and shelf placement. In this work, we propose a novel iCEM+TL framework that explicitly leverages Transfer Learning (TL), where key iCEM parameters are transferred from simpler upstream tasks to guide more complex downstream tasks. Additionally, we applied Reward Redesign (RR) through task decomposition for stacking objects and shelf placement to optimize task-specific performance. Results from the simulation show that our framework achieves success rate improvements of up to 23%. The framework is further validated on a real Franka Emika robot in a stacking task, demonstrating its practical feasibility for real-world deployment.

翻译：随着机器人系统日益复杂，其运动规划模型的复杂度和训练时长显著增加。进化算法（如样本高效的交叉熵方法iCEM）通过有效利用知识重用策略提升性能，近年来在低层实时规划中展现出巨大潜力。尽管iCEM在众多控制任务中表现优异，但在涉及堆叠、滑动及货架放置等更复杂场景时，其性能可能受限。本文提出一种创新的iCEM+TL框架，显式结合迁移学习（TL）技术，将iCEM关键参数从简单上游任务迁移至复杂下游任务以指导规划。此外，针对物体堆叠与货架放置任务，我们通过任务分解实施奖励重设计（RR）以优化任务特定性能。仿真结果表明，该框架的成功率提升高达23%。通过在真实Franka Emika机器人上执行堆叠任务，进一步验证了所提框架在实际部署中的可行性。