Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting

Emlyn Williams; Athanasios Polydoros

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting

Robotics 2025-05-14 v1

Authors: Emlyn Williams , Athanasios Polydoros

Abstract

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.

Keywords

fruit detection

Cite

@article{arxiv.2505.08458,
  title  = {Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting},
  author = {Emlyn Williams and Athanasios Polydoros},
  journal= {arXiv preprint arXiv:2505.08458},
  year   = {2025}
}

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