用强化学习让机械臂零样本采摘草莓，模拟到真实环境无缝迁移。
Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting
- 在自建模拟环境中结合领域随机化训练智能体。
- 通过减少休眠比例算法提升采摘成功率，真实环境表现良好。
- 适合机器人农业、自动采摘方向研究者参考。
本文提出了一套完整的从模拟到真实的自主草莓采摘流程，采用Franka Panda机械臂完成密集果簇的采摘任务。方法基于自定义的Mujoco仿真环境，集成领域随机化技术，利用休眠比例最小化算法训练深度强化学习智能体。该流程将底层控制与高层感知决策相融合，在仿真和真实实验室环境中均表现出良好性能，为实现真实世界自主水果采摘奠定了基础。
原文摘要 · Abstract (English)
This paper presents a comprehensive sim-to-real pipeline for autonomous strawberry picking from dense clusters using a Franka Panda robot. Our approach leverages a custom Mujoco simulation environment that integrates domain randomization techniques. In this environment, a deep reinforcement learning agent is trained using the dormant ratio minimization algorithm. The proposed pipeline bridges low-level control with high-level perception and decision making, demonstrating promising performance in both simulation and in a real laboratory environment, laying the groundwork for successful transfer to real-world autonomous fruit harvesting.
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