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Compliant Motion Planning Integrating Human Skill for Robotic Arm Collecting Tomato Bunch Based on Improved DDPG

文献类型： 外文期刊

作者： Zhang, Yifan 1 ; Li, Yajun 2 ; Feng, Qingchun 2 ; Sun, Jiahui 2 ; Peng, Chuanlang 2 ; Gao, Liangzheng 2 ; Chen, Liping 2 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

3.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： deep reinforcement learning; demonstration learning; path planning; tomato bunch collection; robotics

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2025 年 14 卷 5 期

页码：

收录情况： SCI

摘要： Dexterous manipulation and gradual placement are crucial for preserving fruit integrity during harvesting. Addressing the limitations of conventional path planning methods in learning manual compliant skills, we propose a novel method for tomato bunch collection that integrates human-robot skill transfer with Deep Deterministic Policy Gradient (DDPG). In our method, a demonstrator manually guided the robotic arm using an existing tomato collection mechanism, with spatial trajectories recorded as demonstration paths. We then developed an enhanced DDPG-Z model that incorporates human skill replay for pre-training, expert reward regression loss to stabilize pre-training, and dynamic step-length returns to balance short- and long-term rewards. Subsequently, the agent was trained to minimize the deviations of key points between the demonstration paths and actual paths, effectively approximating human operations. In a highly realistic simulation environment, our method achieved a 25% improvement in convergence speed, a 10.3% increase in post-convergence reward, and a 51.3% boost in destination accuracy compared to the case without the demonstrations, whereas classical models such as DDPG, SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient) failed to converge within the prescribed episodes. This work provides valuable insights for enhancing the compliant operational performance of agricultural robots.

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