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<p>Deep reinforcement learning (DRL) algorithms have been widely applied to robotic manipulation tasks. However, state-of-the-art DRL methods struggle with complex multi-step tasks and sparse-reward settings due to inefficient exploration and reliance on predefined expert knowledge for sub-goal selection. To address these challenges, we propose a hierarchical reinforcement learning framework that integrates demonstrations to improve sub-goal discovery and exploration efficiency. Specifically, we introduce an object-centered sub-goal generation method that autonomously decomposes tasks into meaningful sub-goals by leveraging demonstration data. Furthermore, we enhance both high- and low-level policy learning through demonstration-guided DRL algorithms, improving sample efficiency and task generalization. We evaluate our approach in an open-source multi-step robotic manipulation environment. Experimental results demonstrate that our framework significantly outperforms state-of-the-art imitation learning and hierarchical reinforcement learning baselines in task completion success rates and learning stability.</p>

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Hierarchical Reinforcement Learning With Demonstration for Long-Horizon Robotic Manipulation

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Ning Zhang,
  • Yongjia Zhao,
  • Minghao Yang,
  • Shuling Dai

摘要

Deep reinforcement learning (DRL) algorithms have been widely applied to robotic manipulation tasks. However, state-of-the-art DRL methods struggle with complex multi-step tasks and sparse-reward settings due to inefficient exploration and reliance on predefined expert knowledge for sub-goal selection. To address these challenges, we propose a hierarchical reinforcement learning framework that integrates demonstrations to improve sub-goal discovery and exploration efficiency. Specifically, we introduce an object-centered sub-goal generation method that autonomously decomposes tasks into meaningful sub-goals by leveraging demonstration data. Furthermore, we enhance both high- and low-level policy learning through demonstration-guided DRL algorithms, improving sample efficiency and task generalization. We evaluate our approach in an open-source multi-step robotic manipulation environment. Experimental results demonstrate that our framework significantly outperforms state-of-the-art imitation learning and hierarchical reinforcement learning baselines in task completion success rates and learning stability.