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

Abstract

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.